Overview

Brought to you by YData

Dataset statistics

Number of variables65
Number of observations93
Missing cells1779
Missing cells (%)29.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory246.2 KiB
Average record size in memory2.6 KiB

Variable types

Numeric18
DateTime7
Categorical25
Text3
Unsupported9
Boolean3

Alerts

protocol_id has constant value "8" Constant
sample_snow_depth_mm has constant value "50.0" Constant
sample_snow_depth_flag has constant value "measurable" Constant
protocol_name has constant value "Surface Temperature" Constant
protocol_model has constant value "SurfaceTemperature" Constant
protocol_association_name has constant value "surface_temperature" Constant
protocol_alt_name has constant value "Surface Temperature" Constant
protocol_investigation_area has constant value "Atmosphere" Constant
submission_developer_key_id has constant value "5" Constant
protocol_set_name has constant value "Surface Temperature" Constant
protocol_set_code has constant value "9808" Constant
site_is_citizen_science has constant value "False" Constant
PCA_outlier_flag has constant value "True" Constant
developer_key_is_citizen_science is highly overall correlated with developer_key_name and 19 other fieldsHigh correlation
developer_key_name is highly overall correlated with developer_key_is_citizen_science and 21 other fieldsHigh correlation
homogeneous_site_long_length_m is highly overall correlated with homogeneous_site_short_length_m and 18 other fieldsHigh correlation
homogeneous_site_short_length_m is highly overall correlated with homogeneous_site_long_length_m and 17 other fieldsHigh correlation
instrument_type is highly overall correlated with developer_key_is_citizen_science and 29 other fieldsHigh correlation
organizationid is highly overall correlated with instrument_type and 8 other fieldsHigh correlation
site_developer_key_id is highly overall correlated with developer_key_is_citizen_science and 21 other fieldsHigh correlation
site_elevation is highly overall correlated with homogeneous_site_long_length_m and 13 other fieldsHigh correlation
site_id is highly overall correlated with developer_key_is_citizen_science and 12 other fieldsHigh correlation
site_latitude is highly overall correlated with instrument_type and 9 other fieldsHigh correlation
site_location_source is highly overall correlated with developer_key_is_citizen_science and 20 other fieldsHigh correlation
site_longitude is highly overall correlated with homogeneous_site_long_length_m and 14 other fieldsHigh correlation
site_name is highly overall correlated with developer_key_is_citizen_science and 29 other fieldsHigh correlation
site_photo_photo_data is highly overall correlated with developer_key_is_citizen_science and 27 other fieldsHigh correlation
site_photo_primary_photo_url is highly overall correlated with developer_key_is_citizen_science and 27 other fieldsHigh correlation
site_photo_primary_thumb_url is highly overall correlated with developer_key_is_citizen_science and 27 other fieldsHigh correlation
site_point is highly overall correlated with developer_key_is_citizen_science and 29 other fieldsHigh correlation
st_id is highly overall correlated with developer_key_is_citizen_science and 14 other fieldsHigh correlation
sts_id is highly overall correlated with developer_key_is_citizen_science and 14 other fieldsHigh correlation
submission_data is highly overall correlated with developer_key_is_citizen_science and 25 other fieldsHigh correlation
submission_elevation is highly overall correlated with developer_key_is_citizen_science and 21 other fieldsHigh correlation
submission_id is highly overall correlated with developer_key_is_citizen_science and 18 other fieldsHigh correlation
submission_latitude is highly overall correlated with developer_key_is_citizen_science and 14 other fieldsHigh correlation
submission_longitude is highly overall correlated with developer_key_is_citizen_science and 19 other fieldsHigh correlation
submission_point is highly overall correlated with developer_key_is_citizen_science and 30 other fieldsHigh correlation
surface_condition is highly overall correlated with instrument_type and 8 other fieldsHigh correlation
surface_cover_type is highly overall correlated with developer_key_name and 14 other fieldsHigh correlation
user_type_description is highly overall correlated with developer_key_name and 14 other fieldsHigh correlation
userid is highly overall correlated with developer_key_is_citizen_science and 16 other fieldsHigh correlation
usertype is highly overall correlated with developer_key_name and 14 other fieldsHigh correlation
version is highly overall correlated with site_photo_photo_data and 4 other fieldsHigh correlation
version_id is highly overall correlated with developer_key_is_citizen_science and 19 other fieldsHigh correlation
site_location_source is highly imbalanced (61.5%) Imbalance
site_developer_key_id is highly imbalanced (57.5%) Imbalance
developer_key_name is highly imbalanced (57.5%) Imbalance
surface_condition has 3 (3.2%) missing values Missing
submission_id has 22 (23.7%) missing values Missing
sample_snow_depth_mm has 92 (98.9%) missing values Missing
sample_snow_depth_flag has 92 (98.9%) missing values Missing
site_version_comments has 87 (93.5%) missing values Missing
homogeneous_site_short_length_m has 8 (8.6%) missing values Missing
homogeneous_site_long_length_m has 8 (8.6%) missing values Missing
surface_cover_type has 18 (19.4%) missing values Missing
instrument_type has 44 (47.3%) missing values Missing
submission_comments has 88 (94.6%) missing values Missing
submission_developer_key_id has 22 (23.7%) missing values Missing
submission_access_code_id has 93 (100.0%) missing values Missing
submission_latitude has 22 (23.7%) missing values Missing
submission_longitude has 22 (23.7%) missing values Missing
submission_elevation has 22 (23.7%) missing values Missing
submission_point has 22 (23.7%) missing values Missing
submission_data has 50 (53.8%) missing values Missing
protocol_set_name has 22 (23.7%) missing values Missing
protocol_set_code has 22 (23.7%) missing values Missing
site_deactivated_at has 93 (100.0%) missing values Missing
site_comments has 93 (100.0%) missing values Missing
site_elevation_type has 93 (100.0%) missing values Missing
site_nickname has 93 (100.0%) missing values Missing
site_true_latitude has 93 (100.0%) missing values Missing
site_true_longitude has 93 (100.0%) missing values Missing
site_true_elevation has 93 (100.0%) missing values Missing
site_true_point has 93 (100.0%) missing values Missing
site_photo_measured_at has 69 (74.2%) missing values Missing
site_photo_primary_thumb_url has 69 (74.2%) missing values Missing
site_photo_primary_photo_url has 69 (74.2%) missing values Missing
site_photo_photo_data has 69 (74.2%) missing values Missing
sts_id has unique values Unique
submission_access_code_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
site_deactivated_at is an unsupported type, check if it needs cleaning or further analysis Unsupported
site_comments is an unsupported type, check if it needs cleaning or further analysis Unsupported
site_elevation_type is an unsupported type, check if it needs cleaning or further analysis Unsupported
site_nickname is an unsupported type, check if it needs cleaning or further analysis Unsupported
site_true_latitude is an unsupported type, check if it needs cleaning or further analysis Unsupported
site_true_longitude is an unsupported type, check if it needs cleaning or further analysis Unsupported
site_true_elevation is an unsupported type, check if it needs cleaning or further analysis Unsupported
site_true_point is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-07-07 20:07:38.702646
Analysis finished2025-07-07 20:07:49.927785
Duration11.23 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

st_id
Real number (ℝ)

High correlation 

Distinct92
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145583.61
Minimum42643
Maximum166807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:49.954491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum42643
5-th percentile51435
Q1153275
median157756
Q3163163
95-th percentile166330.4
Maximum166807
Range124164
Interquartile range (IQR)9888

Descriptive statistics

Standard deviation35301.546
Coefficient of variation (CV)0.24248297
Kurtosis3.2094064
Mean145583.61
Median Absolute Deviation (MAD)5407
Skewness-2.1984935
Sum13539276
Variance1.2461992 × 109
MonotonicityNot monotonic
2025-07-07T16:07:50.000958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162456 2
 
2.2%
42643 1
 
1.1%
162509 1
 
1.1%
165623 1
 
1.1%
160012 1
 
1.1%
159875 1
 
1.1%
157732 1
 
1.1%
157494 1
 
1.1%
155967 1
 
1.1%
155964 1
 
1.1%
Other values (82) 82
88.2%
ValueCountFrequency (%)
42643 1
1.1%
43587 1
1.1%
45187 1
1.1%
50235 1
1.1%
50682 1
1.1%
51937 1
1.1%
52383 1
1.1%
56004 1
1.1%
56103 1
1.1%
74313 1
1.1%
ValueCountFrequency (%)
166807 1
1.1%
166802 1
1.1%
166718 1
1.1%
166612 1
1.1%
166511 1
1.1%
166210 1
1.1%
166173 1
1.1%
165703 1
1.1%
165623 1
1.1%
165588 1
1.1%

site_id
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253296
Minimum33276
Maximum381001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:50.038854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33276
5-th percentile33276
Q1125549
median306039
Q3363232
95-th percentile374945.4
Maximum381001
Range347725
Interquartile range (IQR)237683

Descriptive statistics

Standard deviation122118.58
Coefficient of variation (CV)0.48211809
Kurtosis-1.1518269
Mean253296
Median Absolute Deviation (MAD)59560
Skewness-0.66622824
Sum23556528
Variance1.4912948 × 1010
MonotonicityNot monotonic
2025-07-07T16:07:50.077976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
125549 10
 
10.8%
363231 7
 
7.5%
33276 6
 
6.5%
363232 6
 
6.5%
306039 5
 
5.4%
334793 5
 
5.4%
46717 4
 
4.3%
306038 3
 
3.2%
103775 3
 
3.2%
371722 2
 
2.2%
Other values (32) 42
45.2%
ValueCountFrequency (%)
33276 6
6.5%
44993 1
 
1.1%
46717 4
 
4.3%
53367 1
 
1.1%
53371 1
 
1.1%
102060 1
 
1.1%
103775 3
 
3.2%
125549 10
10.8%
126132 1
 
1.1%
139815 1
 
1.1%
ValueCountFrequency (%)
381001 1
1.1%
375282 2
2.2%
374976 2
2.2%
374925 1
1.1%
374721 1
1.1%
371722 2
2.2%
370197 1
1.1%
367215 2
2.2%
366676 1
1.1%
365599 2
2.2%
Distinct91
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2018-02-16 09:49:00
Maximum2025-03-26 17:05:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T16:07:50.118953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:50.162818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

protocol_id
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
8
93 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters93
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 93
100.0%

Length

2025-07-07T16:07:50.197875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:50.214451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8 93
100.0%

Most occurring characters

ValueCountFrequency (%)
8 93
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 93
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 93
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 93
100.0%

userid
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97050238
Minimum11763800
Maximum1.4827748 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:50.239449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11763800
5-th percentile18536109
Q131943308
median1.1830772 × 108
Q31.3604906 × 108
95-th percentile1.4311326 × 108
Maximum1.4827748 × 108
Range1.3651368 × 108
Interquartile range (IQR)1.0410575 × 108

Descriptive statistics

Standard deviation49409602
Coefficient of variation (CV)0.50911367
Kurtosis-1.1978334
Mean97050238
Median Absolute Deviation (MAD)20582320
Skewness-0.73836034
Sum9.0256721 × 109
Variance2.4413088 × 1015
MonotonicityNot monotonic
2025-07-07T16:07:50.280051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20826653 10
 
10.8%
126538334 5
 
5.4%
114796529 5
 
5.4%
143113246 4
 
4.3%
22264898 4
 
4.3%
11763800 3
 
3.2%
116319735 3
 
3.2%
135978772 3
 
3.2%
98640025 2
 
2.2%
139178912 2
 
2.2%
Other values (41) 52
55.9%
ValueCountFrequency (%)
11763800 3
 
3.2%
14393910 1
 
1.1%
18536109 2
 
2.2%
20826653 10
10.8%
22264898 4
 
4.3%
25030021 1
 
1.1%
25166665 1
 
1.1%
28881701 1
 
1.1%
31943308 1
 
1.1%
43183801 1
 
1.1%
ValueCountFrequency (%)
148277476 1
 
1.1%
148206535 1
 
1.1%
143172442 1
 
1.1%
143113288 1
 
1.1%
143113260 2
2.2%
143113246 4
4.3%
143113092 1
 
1.1%
142150439 1
 
1.1%
139178912 2
2.2%
138890065 1
 
1.1%

surface_condition
Categorical

High correlation  Missing 

Distinct3
Distinct (%)3.3%
Missing3
Missing (%)3.2%
Memory size5.5 KiB
dry
62 
wet
27 
snow
 
1

Length

Max length4
Median length3
Mean length3.0111111
Min length3

Characters and Unicode

Total characters271
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st rowwet
2nd rowdry
3rd rowsnow
4th rowdry
5th rowwet

Common Values

ValueCountFrequency (%)
dry 62
66.7%
wet 27
29.0%
snow 1
 
1.1%
(Missing) 3
 
3.2%

Length

2025-07-07T16:07:50.314356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:50.334047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
dry 62
68.9%
wet 27
30.0%
snow 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
d 62
22.9%
r 62
22.9%
y 62
22.9%
w 28
10.3%
e 27
10.0%
t 27
10.0%
s 1
 
0.4%
n 1
 
0.4%
o 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 62
22.9%
r 62
22.9%
y 62
22.9%
w 28
10.3%
e 27
10.0%
t 27
10.0%
s 1
 
0.4%
n 1
 
0.4%
o 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 62
22.9%
r 62
22.9%
y 62
22.9%
w 28
10.3%
e 27
10.0%
t 27
10.0%
s 1
 
0.4%
n 1
 
0.4%
o 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 62
22.9%
r 62
22.9%
y 62
22.9%
w 28
10.3%
e 27
10.0%
t 27
10.0%
s 1
 
0.4%
n 1
 
0.4%
o 1
 
0.4%

organizationid
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72344437
Minimum214108
Maximum1.3917893 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:50.361117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum214108
5-th percentile7039551.6
Q120826740
median85217994
Q31.1479654 × 108
95-th percentile1.3651725 × 108
Maximum1.3917893 × 108
Range1.3896482 × 108
Interquartile range (IQR)93969804

Descriptive statistics

Standard deviation48008945
Coefficient of variation (CV)0.66361627
Kurtosis-1.5237585
Mean72344437
Median Absolute Deviation (MAD)49225895
Skewness-0.044309743
Sum6.7280326 × 109
Variance2.3048588 × 1015
MonotonicityNot monotonic
2025-07-07T16:07:50.399255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
134443889 13
 
14.0%
20826740 10
 
10.8%
98767129 8
 
8.6%
8220745 6
 
6.5%
85217994 6
 
6.5%
114796544 5
 
5.4%
22884960 4
 
4.3%
11763857 3
 
3.2%
69841683 3
 
3.2%
98640058 2
 
2.2%
Other values (24) 33
35.5%
ValueCountFrequency (%)
214108 1
 
1.1%
6508393 2
 
2.2%
6509997 2
 
2.2%
7392588 1
 
1.1%
8220745 6
6.5%
11763857 3
 
3.2%
20826740 10
10.8%
22884960 4
 
4.3%
25030112 2
 
2.2%
31941878 1
 
1.1%
ValueCountFrequency (%)
139178931 2
 
2.2%
137798950 1
 
1.1%
137603671 2
 
2.2%
135792971 1
 
1.1%
134443889 13
14.0%
130827348 1
 
1.1%
121494950 2
 
2.2%
114796544 5
 
5.4%
98767129 8
8.6%
98766834 1
 
1.1%

usertype
Categorical

High correlation 

Distinct3
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
11
46 
21
45 
-1
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters186
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 46
49.5%
21 45
48.4%
-1 2
 
2.2%

Length

2025-07-07T16:07:50.432744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:50.451833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11 46
49.5%
21 45
48.4%
1 2
 
2.2%

Most occurring characters

ValueCountFrequency (%)
1 139
74.7%
2 45
 
24.2%
- 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 139
74.7%
2 45
 
24.2%
- 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 139
74.7%
2 45
 
24.2%
- 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 139
74.7%
2 45
 
24.2%
- 2
 
1.1%

submission_id
Real number (ℝ)

High correlation  Missing 

Distinct71
Distinct (%)100.0%
Missing22
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean55259972
Minimum42057533
Maximum59871769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:50.481600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum42057533
5-th percentile49421538
Q153416587
median55412805
Q358376832
95-th percentile59718795
Maximum59871769
Range17814236
Interquartile range (IQR)4960245.5

Descriptive statistics

Standard deviation3726322.1
Coefficient of variation (CV)0.067432573
Kurtosis0.73310652
Mean55259972
Median Absolute Deviation (MAD)2808111
Skewness-0.84772381
Sum3.923458 × 109
Variance1.3885476 × 1013
MonotonicityNot monotonic
2025-07-07T16:07:50.523302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59144688 1
 
1.1%
54682678 1
 
1.1%
54486573 1
 
1.1%
54275843 1
 
1.1%
53608514 1
 
1.1%
53201717 1
 
1.1%
59360750 1
 
1.1%
56092934 1
 
1.1%
54651066 1
 
1.1%
54486574 1
 
1.1%
Other values (61) 61
65.6%
(Missing) 22
 
23.7%
ValueCountFrequency (%)
42057533 1
1.1%
49032344 1
1.1%
49033202 1
1.1%
49344078 1
1.1%
49498997 1
1.1%
49501504 1
1.1%
49502399 1
1.1%
49523013 1
1.1%
49706110 1
1.1%
49899137 1
1.1%
ValueCountFrequency (%)
59871769 1
1.1%
59871204 1
1.1%
59777419 1
1.1%
59764579 1
1.1%
59673011 1
1.1%
59582324 1
1.1%
59529735 1
1.1%
59403497 1
1.1%
59360750 1
1.1%
59344300 1
1.1%
Distinct92
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2018-02-16 15:54:49.216835
Maximum2025-03-26 17:25:19.242881
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T16:07:50.563630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:50.607033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct92
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2018-02-16 15:54:49.216835
Maximum2025-03-26 17:25:19.242881
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T16:07:50.648337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:50.691733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sts_id
Real number (ℝ)

High correlation  Unique 

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean655035.9
Minimum284280
Maximum728899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:50.732176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum284280
5-th percentile326285.8
Q1680782
median697803
Q3715351
95-th percentile727631
Maximum728899
Range444619
Interquartile range (IQR)34569

Descriptive statistics

Standard deviation122705.07
Coefficient of variation (CV)0.18732571
Kurtosis3.4127774
Mean655035.9
Median Absolute Deviation (MAD)17548
Skewness-2.2297003
Sum60918339
Variance1.5056534 × 1010
MonotonicityNot monotonic
2025-07-07T16:07:50.772902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
284280 1
 
1.1%
713196 1
 
1.1%
725068 1
 
1.1%
705485 1
 
1.1%
705100 1
 
1.1%
697758 1
 
1.1%
696622 1
 
1.1%
690948 1
 
1.1%
690918 1
 
1.1%
688728 1
 
1.1%
Other values (83) 83
89.2%
ValueCountFrequency (%)
284280 1
1.1%
287325 1
1.1%
293923 1
1.1%
319356 1
1.1%
321958 1
1.1%
329171 1
1.1%
331900 1
1.1%
351530 1
1.1%
352128 1
1.1%
420889 1
1.1%
ValueCountFrequency (%)
728899 1
1.1%
728867 1
1.1%
728623 1
1.1%
728476 1
1.1%
728120 1
1.1%
727305 1
1.1%
727106 1
1.1%
725440 1
1.1%
725068 1
1.1%
724869 1
1.1%

sample_number
Real number (ℝ)

Distinct9
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3763441
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:50.802713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.835773
Coefficient of variation (CV)0.6479776
Kurtosis-1.3812841
Mean4.3763441
Median Absolute Deviation (MAD)3
Skewness0.14260703
Sum407
Variance8.0416082
MonotonicityNot monotonic
2025-07-07T16:07:50.830986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 27
29.0%
6 12
12.9%
5 11
11.8%
9 9
 
9.7%
7 9
 
9.7%
8 7
 
7.5%
2 7
 
7.5%
4 6
 
6.5%
3 5
 
5.4%
ValueCountFrequency (%)
1 27
29.0%
2 7
 
7.5%
3 5
 
5.4%
4 6
 
6.5%
5 11
11.8%
6 12
12.9%
7 9
 
9.7%
8 7
 
7.5%
9 9
 
9.7%
ValueCountFrequency (%)
9 9
 
9.7%
8 7
 
7.5%
7 9
 
9.7%
6 12
12.9%
5 11
11.8%
4 6
 
6.5%
3 5
 
5.4%
2 7
 
7.5%
1 27
29.0%
Distinct82
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.197527
Minimum-11.9
Maximum59.2
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)8.6%
Memory size1.5 KiB
2025-07-07T16:07:50.865196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-11.9
5-th percentile-2.16
Q17.1
median17
Q325.9
95-th percentile39.96
Maximum59.2
Range71.1
Interquartile range (IQR)18.8

Descriptive statistics

Standard deviation13.572705
Coefficient of variation (CV)0.78922425
Kurtosis0.1174666
Mean17.197527
Median Absolute Deviation (MAD)9.6
Skewness0.31676253
Sum1599.37
Variance184.21833
MonotonicityNot monotonic
2025-07-07T16:07:50.905498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.5 3
 
3.2%
0.6 3
 
3.2%
19.1 2
 
2.2%
27.9 2
 
2.2%
15 2
 
2.2%
17 2
 
2.2%
7.9 2
 
2.2%
10 2
 
2.2%
26.9 2
 
2.2%
1.2 1
 
1.1%
Other values (72) 72
77.4%
ValueCountFrequency (%)
-11.9 1
 
1.1%
-9.6 1
 
1.1%
-9.2 1
 
1.1%
-2.6 1
 
1.1%
-2.4 1
 
1.1%
-2 1
 
1.1%
-1.25 1
 
1.1%
-0.8 1
 
1.1%
0.3 1
 
1.1%
0.6 3
3.2%
ValueCountFrequency (%)
59.2 1
1.1%
48.1 1
1.1%
43.4 1
1.1%
42 1
1.1%
40.2 1
1.1%
39.8 1
1.1%
37.5 1
1.1%
36 1
1.1%
35.6 1
1.1%
34.6 1
1.1%

sample_snow_depth_mm
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing92
Missing (%)98.9%
Memory size5.8 KiB
50.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row50.0

Common Values

ValueCountFrequency (%)
50.0 1
 
1.1%
(Missing) 92
98.9%

Length

2025-07-07T16:07:50.941012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:50.958088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
50.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
50.0%
5 1
25.0%
. 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
50.0%
5 1
25.0%
. 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
50.0%
5 1
25.0%
. 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
50.0%
5 1
25.0%
. 1
25.0%

sample_snow_depth_flag
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing92
Missing (%)98.9%
Memory size5.8 KiB
2025-07-07T16:07:51.002419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowmeasurable
ValueCountFrequency (%)
measurable 1
100.0%
2025-07-07T16:07:51.080494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2
20.0%
a 2
20.0%
m 1
10.0%
s 1
10.0%
u 1
10.0%
r 1
10.0%
b 1
10.0%
l 1
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2
20.0%
a 2
20.0%
m 1
10.0%
s 1
10.0%
u 1
10.0%
r 1
10.0%
b 1
10.0%
l 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2
20.0%
a 2
20.0%
m 1
10.0%
s 1
10.0%
u 1
10.0%
r 1
10.0%
b 1
10.0%
l 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2
20.0%
a 2
20.0%
m 1
10.0%
s 1
10.0%
u 1
10.0%
r 1
10.0%
b 1
10.0%
l 1
10.0%

version_id
Real number (ℝ)

High correlation 

Distinct72
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71145.054
Minimum10345
Maximum105196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:51.115090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10345
5-th percentile10971
Q165162
median78891
Q386571
95-th percentile101825.4
Maximum105196
Range94851
Interquartile range (IQR)21409

Descriptive statistics

Standard deviation26604.525
Coefficient of variation (CV)0.37394764
Kurtosis0.92552544
Mean71145.054
Median Absolute Deviation (MAD)8739
Skewness-1.3746343
Sum6616490
Variance7.0780075 × 108
MonotonicityNot monotonic
2025-07-07T16:07:51.155191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10345 4
 
4.3%
76622 3
 
3.2%
75658 3
 
3.2%
83979 3
 
3.2%
57843 2
 
2.2%
77571 2
 
2.2%
84096 2
 
2.2%
76621 2
 
2.2%
59512 2
 
2.2%
78694 2
 
2.2%
Other values (62) 68
73.1%
ValueCountFrequency (%)
10345 4
4.3%
10794 1
 
1.1%
11089 1
 
1.1%
11364 1
 
1.1%
11563 2
2.2%
11654 1
 
1.1%
11742 1
 
1.1%
12340 1
 
1.1%
21602 1
 
1.1%
47300 1
 
1.1%
ValueCountFrequency (%)
105196 1
1.1%
104994 1
1.1%
104713 1
1.1%
104473 1
1.1%
102507 1
1.1%
101371 2
2.2%
101238 1
1.1%
101057 1
1.1%
91175 1
1.1%
90894 1
1.1%

version
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.58065
Minimum1
Maximum1482
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:51.192366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median16
Q384
95-th percentile1297
Maximum1482
Range1481
Interquartile range (IQR)80

Descriptive statistics

Standard deviation348.96288
Coefficient of variation (CV)2.3806887
Kurtosis8.4029261
Mean146.58065
Median Absolute Deviation (MAD)15
Skewness3.0768296
Sum13632
Variance121775.09
MonotonicityNot monotonic
2025-07-07T16:07:51.232072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 15
 
16.1%
6 6
 
6.5%
2 6
 
6.5%
30 4
 
4.3%
5 3
 
3.2%
1297 3
 
3.2%
91 3
 
3.2%
3 2
 
2.2%
13 2
 
2.2%
7 2
 
2.2%
Other values (37) 47
50.5%
ValueCountFrequency (%)
1 15
16.1%
2 6
 
6.5%
3 2
 
2.2%
4 2
 
2.2%
5 3
 
3.2%
6 6
 
6.5%
7 2
 
2.2%
8 2
 
2.2%
9 2
 
2.2%
11 2
 
2.2%
ValueCountFrequency (%)
1482 1
 
1.1%
1462 1
 
1.1%
1453 1
 
1.1%
1297 3
3.2%
902 1
 
1.1%
466 1
 
1.1%
314 1
 
1.1%
295 1
 
1.1%
286 1
 
1.1%
284 1
 
1.1%
Distinct72
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2017-02-08 19:27:01.982011
Maximum2025-03-24 18:39:45.225967
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T16:07:51.272743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:51.316330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct72
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2017-02-08 19:27:01.982000
Maximum2025-03-24 18:39:45.225959
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T16:07:51.357965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:51.402126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

site_version_comments
Text

Missing 

Distinct4
Distinct (%)66.7%
Missing87
Missing (%)93.5%
Memory size6.2 KiB
2025-07-07T16:07:51.491507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length176
Median length103.5
Mean length47.333333
Min length16

Characters and Unicode

Total characters284
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)50.0%

Sample

1st rowConcrete Surface
2nd rowConcrete Surface
3rd rowConcrete Surface
4th rowauto-sync from atmosphere_sites
5th rowEn el patio del colegio no se cuenta con una superficie homogénea de 30 x 30 por lo que utilizaremos como lugar de medición una superficie menor de aproximadamente 25 m x 20 m.
ValueCountFrequency (%)
concrete 3
 
6.2%
surface 3
 
6.2%
de 3
 
6.2%
una 2
 
4.2%
m 2
 
4.2%
x 2
 
4.2%
superficie 2
 
4.2%
30 2
 
4.2%
del 1
 
2.1%
utilizaremos 1
 
2.1%
Other values (27) 27
56.2%
2025-07-07T16:07:51.612756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42
14.8%
e 34
 
12.0%
o 21
 
7.4%
a 16
 
5.6%
n 16
 
5.6%
r 16
 
5.6%
c 14
 
4.9%
u 13
 
4.6%
t 13
 
4.6%
m 12
 
4.2%
Other values (28) 87
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
42
14.8%
e 34
 
12.0%
o 21
 
7.4%
a 16
 
5.6%
n 16
 
5.6%
r 16
 
5.6%
c 14
 
4.9%
u 13
 
4.6%
t 13
 
4.6%
m 12
 
4.2%
Other values (28) 87
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
42
14.8%
e 34
 
12.0%
o 21
 
7.4%
a 16
 
5.6%
n 16
 
5.6%
r 16
 
5.6%
c 14
 
4.9%
u 13
 
4.6%
t 13
 
4.6%
m 12
 
4.2%
Other values (28) 87
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
42
14.8%
e 34
 
12.0%
o 21
 
7.4%
a 16
 
5.6%
n 16
 
5.6%
r 16
 
5.6%
c 14
 
4.9%
u 13
 
4.6%
t 13
 
4.6%
m 12
 
4.2%
Other values (28) 87
30.6%

homogeneous_site_short_length_m
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)10.6%
Missing8
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean59.164706
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:51.643368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q130
median90
Q390
95-th percentile90
Maximum90
Range88
Interquartile range (IQR)60

Descriptive statistics

Standard deviation35.877054
Coefficient of variation (CV)0.60639284
Kurtosis-1.6243341
Mean59.164706
Median Absolute Deviation (MAD)0
Skewness-0.42881955
Sum5029
Variance1287.163
MonotonicityNot monotonic
2025-07-07T16:07:51.667385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
90 47
50.5%
30 14
 
15.1%
3 8
 
8.6%
22 6
 
6.5%
10 3
 
3.2%
53 3
 
3.2%
2 2
 
2.2%
5 1
 
1.1%
25 1
 
1.1%
(Missing) 8
 
8.6%
ValueCountFrequency (%)
2 2
 
2.2%
3 8
 
8.6%
5 1
 
1.1%
10 3
 
3.2%
22 6
 
6.5%
25 1
 
1.1%
30 14
 
15.1%
53 3
 
3.2%
90 47
50.5%
ValueCountFrequency (%)
90 47
50.5%
53 3
 
3.2%
30 14
 
15.1%
25 1
 
1.1%
22 6
 
6.5%
10 3
 
3.2%
5 1
 
1.1%
3 8
 
8.6%
2 2
 
2.2%

homogeneous_site_long_length_m
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)9.4%
Missing8
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean59.768235
Minimum3
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:51.690364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q130
median90
Q390
95-th percentile90
Maximum90
Range87
Interquartile range (IQR)60

Descriptive statistics

Standard deviation35.578694
Coefficient of variation (CV)0.59527763
Kurtosis-1.6261174
Mean59.768235
Median Absolute Deviation (MAD)0
Skewness-0.43216601
Sum5080.3
Variance1265.8434
MonotonicityNot monotonic
2025-07-07T16:07:51.714751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
90 48
51.6%
30 15
 
16.1%
3 9
 
9.7%
31.55 6
 
6.5%
5 2
 
2.2%
10 2
 
2.2%
22 2
 
2.2%
20 1
 
1.1%
(Missing) 8
 
8.6%
ValueCountFrequency (%)
3 9
 
9.7%
5 2
 
2.2%
10 2
 
2.2%
20 1
 
1.1%
22 2
 
2.2%
30 15
 
16.1%
31.55 6
 
6.5%
90 48
51.6%
ValueCountFrequency (%)
90 48
51.6%
31.55 6
 
6.5%
30 15
 
16.1%
22 2
 
2.2%
20 1
 
1.1%
10 2
 
2.2%
5 2
 
2.2%
3 9
 
9.7%

surface_cover_type
Categorical

High correlation  Missing 

Distinct8
Distinct (%)10.7%
Missing18
Missing (%)19.4%
Memory size6.0 KiB
short grass
40 
asphalt
16 
sand
rocks
 
4
concrete
 
3
Other values (3)

Length

Max length15
Median length11
Mean length9.3333333
Min length4

Characters and Unicode

Total characters700
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st rowconcrete
2nd rowconcrete
3rd rowconcrete
4th rowshort grass
5th rowasphalt

Common Values

ValueCountFrequency (%)
short grass 40
43.0%
asphalt 16
 
17.2%
sand 6
 
6.5%
rocks 4
 
4.3%
concrete 3
 
3.2%
dry bare ground 3
 
3.2%
tall grass 2
 
2.2%
artificial turf 1
 
1.1%
(Missing) 18
19.4%

Length

2025-07-07T16:07:51.745470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:51.771602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
grass 42
33.9%
short 40
32.3%
asphalt 16
 
12.9%
sand 6
 
4.8%
rocks 4
 
3.2%
concrete 3
 
2.4%
dry 3
 
2.4%
bare 3
 
2.4%
ground 3
 
2.4%
tall 2
 
1.6%
Other values (2) 2
 
1.6%

Most occurring characters

ValueCountFrequency (%)
s 150
21.4%
r 100
14.3%
a 87
12.4%
t 63
9.0%
h 56
 
8.0%
o 50
 
7.1%
49
 
7.0%
g 45
 
6.4%
l 21
 
3.0%
p 16
 
2.3%
Other values (10) 63
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 150
21.4%
r 100
14.3%
a 87
12.4%
t 63
9.0%
h 56
 
8.0%
o 50
 
7.1%
49
 
7.0%
g 45
 
6.4%
l 21
 
3.0%
p 16
 
2.3%
Other values (10) 63
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 150
21.4%
r 100
14.3%
a 87
12.4%
t 63
9.0%
h 56
 
8.0%
o 50
 
7.1%
49
 
7.0%
g 45
 
6.4%
l 21
 
3.0%
p 16
 
2.3%
Other values (10) 63
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 150
21.4%
r 100
14.3%
a 87
12.4%
t 63
9.0%
h 56
 
8.0%
o 50
 
7.1%
49
 
7.0%
g 45
 
6.4%
l 21
 
3.0%
p 16
 
2.3%
Other values (10) 63
9.0%

instrument_type
Categorical

High correlation  Missing 

Distinct18
Distinct (%)36.7%
Missing44
Missing (%)47.3%
Memory size6.3 KiB
Raytech ST20
12 
Fluke and Ektech
Eventek GM9053E
Etekcity
Fluke & Ektech
Other values (13)
19 

Length

Max length78
Median length36
Mean length17.346939
Min length5

Characters and Unicode

Total characters850
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)18.4%

Sample

1st rowBenetech GM3200
2nd rowBenetech GM3200
3rd rowBenetech GM3200
4th rowEtekcity 749
5th rowEtekcity774

Common Values

ValueCountFrequency (%)
Raytech ST20 12
 
12.9%
Fluke and Ektech 7
 
7.5%
Eventek GM9053E 4
 
4.3%
Etekcity 4
 
4.3%
Fluke & Ektech 3
 
3.2%
Fluke & Ektech 3
 
3.2%
Benetech GM3200 3
 
3.2%
Infrared thermometer 2
 
2.2%
Infrared Thermometer (LASERGRIP 749) 2
 
2.2%
FLUKE 1
 
1.1%
Other values (8) 8
 
8.6%
(Missing) 44
47.3%

Length

2025-07-07T16:07:51.812012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fluke 16
 
12.4%
ektech 13
 
10.1%
raytech 12
 
9.3%
st20 12
 
9.3%
etekcity 8
 
6.2%
and 7
 
5.4%
infrared 4
 
3.1%
eventek 4
 
3.1%
gm9053e 4
 
3.1%
thermometer 4
 
3.1%
Other values (25) 45
34.9%

Most occurring characters

ValueCountFrequency (%)
e 92
 
10.8%
80
 
9.4%
t 56
 
6.6%
k 38
 
4.5%
a 37
 
4.4%
c 36
 
4.2%
E 36
 
4.2%
h 32
 
3.8%
r 31
 
3.6%
0 29
 
3.4%
Other values (46) 383
45.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 92
 
10.8%
80
 
9.4%
t 56
 
6.6%
k 38
 
4.5%
a 37
 
4.4%
c 36
 
4.2%
E 36
 
4.2%
h 32
 
3.8%
r 31
 
3.6%
0 29
 
3.4%
Other values (46) 383
45.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 92
 
10.8%
80
 
9.4%
t 56
 
6.6%
k 38
 
4.5%
a 37
 
4.4%
c 36
 
4.2%
E 36
 
4.2%
h 32
 
3.8%
r 31
 
3.6%
0 29
 
3.4%
Other values (46) 383
45.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 92
 
10.8%
80
 
9.4%
t 56
 
6.6%
k 38
 
4.5%
a 37
 
4.4%
c 36
 
4.2%
E 36
 
4.2%
h 32
 
3.8%
r 31
 
3.6%
0 29
 
3.4%
Other values (46) 383
45.1%

protocol_name
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
Surface Temperature
93 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1767
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSurface Temperature
2nd rowSurface Temperature
3rd rowSurface Temperature
4th rowSurface Temperature
5th rowSurface Temperature

Common Values

ValueCountFrequency (%)
Surface Temperature 93
100.0%

Length

2025-07-07T16:07:51.842368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:51.859412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surface 93
50.0%
temperature 93
50.0%

Most occurring characters

ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
S 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
93
 
5.3%
T 93
 
5.3%
m 93
 
5.3%
Other values (2) 186
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
S 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
93
 
5.3%
T 93
 
5.3%
m 93
 
5.3%
Other values (2) 186
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
S 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
93
 
5.3%
T 93
 
5.3%
m 93
 
5.3%
Other values (2) 186
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
S 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
93
 
5.3%
T 93
 
5.3%
m 93
 
5.3%
Other values (2) 186
10.5%

protocol_model
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
SurfaceTemperature
93 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters1674
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSurfaceTemperature
2nd rowSurfaceTemperature
3rd rowSurfaceTemperature
4th rowSurfaceTemperature
5th rowSurfaceTemperature

Common Values

ValueCountFrequency (%)
SurfaceTemperature 93
100.0%

Length

2025-07-07T16:07:51.880827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:51.897400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surfacetemperature 93
100.0%

Most occurring characters

ValueCountFrequency (%)
e 372
22.2%
r 279
16.7%
u 186
11.1%
a 186
11.1%
S 93
 
5.6%
f 93
 
5.6%
c 93
 
5.6%
T 93
 
5.6%
m 93
 
5.6%
p 93
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 372
22.2%
r 279
16.7%
u 186
11.1%
a 186
11.1%
S 93
 
5.6%
f 93
 
5.6%
c 93
 
5.6%
T 93
 
5.6%
m 93
 
5.6%
p 93
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 372
22.2%
r 279
16.7%
u 186
11.1%
a 186
11.1%
S 93
 
5.6%
f 93
 
5.6%
c 93
 
5.6%
T 93
 
5.6%
m 93
 
5.6%
p 93
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 372
22.2%
r 279
16.7%
u 186
11.1%
a 186
11.1%
S 93
 
5.6%
f 93
 
5.6%
c 93
 
5.6%
T 93
 
5.6%
m 93
 
5.6%
p 93
 
5.6%

protocol_association_name
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
surface_temperature
93 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1767
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsurface_temperature
2nd rowsurface_temperature
3rd rowsurface_temperature
4th rowsurface_temperature
5th rowsurface_temperature

Common Values

ValueCountFrequency (%)
surface_temperature 93
100.0%

Length

2025-07-07T16:07:51.919242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:51.935897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surface_temperature 93
100.0%

Most occurring characters

ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
t 186
10.5%
s 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
_ 93
 
5.3%
m 93
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
t 186
10.5%
s 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
_ 93
 
5.3%
m 93
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
t 186
10.5%
s 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
_ 93
 
5.3%
m 93
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
t 186
10.5%
s 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
_ 93
 
5.3%
m 93
 
5.3%

protocol_alt_name
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
Surface Temperature
93 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1767
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSurface Temperature
2nd rowSurface Temperature
3rd rowSurface Temperature
4th rowSurface Temperature
5th rowSurface Temperature

Common Values

ValueCountFrequency (%)
Surface Temperature 93
100.0%

Length

2025-07-07T16:07:51.957411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:52.261419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surface 93
50.0%
temperature 93
50.0%

Most occurring characters

ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
S 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
93
 
5.3%
T 93
 
5.3%
m 93
 
5.3%
Other values (2) 186
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
S 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
93
 
5.3%
T 93
 
5.3%
m 93
 
5.3%
Other values (2) 186
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
S 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
93
 
5.3%
T 93
 
5.3%
m 93
 
5.3%
Other values (2) 186
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 372
21.1%
r 279
15.8%
u 186
10.5%
a 186
10.5%
S 93
 
5.3%
f 93
 
5.3%
c 93
 
5.3%
93
 
5.3%
T 93
 
5.3%
m 93
 
5.3%
Other values (2) 186
10.5%

protocol_investigation_area
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Atmosphere
93 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters930
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAtmosphere
2nd rowAtmosphere
3rd rowAtmosphere
4th rowAtmosphere
5th rowAtmosphere

Common Values

ValueCountFrequency (%)
Atmosphere 93
100.0%

Length

2025-07-07T16:07:52.285938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:52.305541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
atmosphere 93
100.0%

Most occurring characters

ValueCountFrequency (%)
e 186
20.0%
A 93
10.0%
t 93
10.0%
m 93
10.0%
o 93
10.0%
s 93
10.0%
p 93
10.0%
h 93
10.0%
r 93
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 186
20.0%
A 93
10.0%
t 93
10.0%
m 93
10.0%
o 93
10.0%
s 93
10.0%
p 93
10.0%
h 93
10.0%
r 93
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 186
20.0%
A 93
10.0%
t 93
10.0%
m 93
10.0%
o 93
10.0%
s 93
10.0%
p 93
10.0%
h 93
10.0%
r 93
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 186
20.0%
A 93
10.0%
t 93
10.0%
m 93
10.0%
o 93
10.0%
s 93
10.0%
p 93
10.0%
h 93
10.0%
r 93
10.0%

user_type_description
Categorical

High correlation 

Distinct3
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
non-student user - trained
46 
student user - trained
45 
not categorized
 
2

Length

Max length26
Median length22
Mean length23.827957
Min length15

Characters and Unicode

Total characters2216
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot categorized
2nd rownot categorized
3rd rownon-student user - trained
4th rownon-student user - trained
5th rownon-student user - trained

Common Values

ValueCountFrequency (%)
non-student user - trained 46
49.5%
student user - trained 45
48.4%
not categorized 2
 
2.2%

Length

2025-07-07T16:07:52.330511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:52.353415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
user 91
24.7%
91
24.7%
trained 91
24.7%
non-student 46
12.5%
student 45
12.2%
not 2
 
0.5%
categorized 2
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t 277
12.5%
e 277
12.5%
n 276
12.5%
275
12.4%
d 184
8.3%
r 184
8.3%
s 182
8.2%
u 182
8.2%
- 137
6.2%
a 93
 
4.2%
Other values (5) 149
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 277
12.5%
e 277
12.5%
n 276
12.5%
275
12.4%
d 184
8.3%
r 184
8.3%
s 182
8.2%
u 182
8.2%
- 137
6.2%
a 93
 
4.2%
Other values (5) 149
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 277
12.5%
e 277
12.5%
n 276
12.5%
275
12.4%
d 184
8.3%
r 184
8.3%
s 182
8.2%
u 182
8.2%
- 137
6.2%
a 93
 
4.2%
Other values (5) 149
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 277
12.5%
e 277
12.5%
n 276
12.5%
275
12.4%
d 184
8.3%
r 184
8.3%
s 182
8.2%
u 182
8.2%
- 137
6.2%
a 93
 
4.2%
Other values (5) 149
6.7%

submission_comments
Text

Missing 

Distinct5
Distinct (%)100.0%
Missing88
Missing (%)94.6%
Memory size5.9 KiB
2025-07-07T16:07:52.413567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length67
Median length26
Mean length23
Min length6

Characters and Unicode

Total characters115
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowThe snow was slightly warmer, the closer it was to the parking lot.
2nd rowcold, snowy, windy, cloudy
3rd rowPretty hot
4th rowCore 4
5th rowCore 2
ValueCountFrequency (%)
the 3
 
13.0%
was 2
 
8.7%
core 2
 
8.7%
cold 1
 
4.3%
4 1
 
4.3%
hot 1
 
4.3%
pretty 1
 
4.3%
cloudy 1
 
4.3%
windy 1
 
4.3%
snowy 1
 
4.3%
Other values (9) 9
39.1%
2025-07-07T16:07:52.510934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
15.7%
o 10
 
8.7%
t 9
 
7.8%
e 8
 
7.0%
r 7
 
6.1%
s 6
 
5.2%
w 6
 
5.2%
l 6
 
5.2%
y 5
 
4.3%
h 5
 
4.3%
Other values (17) 35
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18
15.7%
o 10
 
8.7%
t 9
 
7.8%
e 8
 
7.0%
r 7
 
6.1%
s 6
 
5.2%
w 6
 
5.2%
l 6
 
5.2%
y 5
 
4.3%
h 5
 
4.3%
Other values (17) 35
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18
15.7%
o 10
 
8.7%
t 9
 
7.8%
e 8
 
7.0%
r 7
 
6.1%
s 6
 
5.2%
w 6
 
5.2%
l 6
 
5.2%
y 5
 
4.3%
h 5
 
4.3%
Other values (17) 35
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18
15.7%
o 10
 
8.7%
t 9
 
7.8%
e 8
 
7.0%
r 7
 
6.1%
s 6
 
5.2%
w 6
 
5.2%
l 6
 
5.2%
y 5
 
4.3%
h 5
 
4.3%
Other values (17) 35
30.4%

submission_developer_key_id
Categorical

Constant  Missing 

Distinct1
Distinct (%)1.4%
Missing22
Missing (%)23.7%
Memory size5.4 KiB
5
71 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters71
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 71
76.3%
(Missing) 22
 
23.7%

Length

2025-07-07T16:07:52.548342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:52.567283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 71
100.0%

Most occurring characters

ValueCountFrequency (%)
5 71
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 71
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 71
100.0%

submission_access_code_id
Unsupported

Missing  Rejected  Unsupported 

Missing93
Missing (%)100.0%
Memory size1.5 KiB

submission_latitude
Real number (ℝ)

High correlation  Missing 

Distinct29
Distinct (%)40.8%
Missing22
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean34.411815
Minimum-4.0957
Maximum53.1957
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)2.2%
Memory size1.5 KiB
2025-07-07T16:07:52.588000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.0957
5-th percentile15.814535
Q130.86929
median36.290455
Q341.613645
95-th percentile44.1143
Maximum53.1957
Range57.2914
Interquartile range (IQR)10.744355

Descriptive statistics

Standard deviation10.691181
Coefficient of variation (CV)0.31068344
Kurtosis3.8882847
Mean34.411815
Median Absolute Deviation (MAD)5.374265
Skewness-1.6634627
Sum2443.2389
Variance114.30135
MonotonicityNot monotonic
2025-07-07T16:07:52.622146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
36.290455 10
 
10.8%
24.681796 6
 
6.5%
41.56102 6
 
6.5%
41.56257 6
 
6.5%
32.0849 5
 
5.4%
32.0848 3
 
3.2%
44.1143 2
 
2.2%
41.665799 2
 
2.2%
42.8244 2
 
2.2%
30.86929 2
 
2.2%
Other values (19) 27
29.0%
(Missing) 22
23.7%
ValueCountFrequency (%)
-4.0957 2
 
2.2%
7.27897 2
 
2.2%
24.3501 1
 
1.1%
24.681796 6
6.5%
24.9752 1
 
1.1%
24.9788 1
 
1.1%
26.603819 2
 
2.2%
27.12228 1
 
1.1%
30.3188 1
 
1.1%
30.86929 2
 
2.2%
ValueCountFrequency (%)
53.1957 1
1.1%
49.327485 2
2.2%
44.1143 2
2.2%
44.1138 1
1.1%
42.8244 2
2.2%
41.71179 2
2.2%
41.66953 2
2.2%
41.665799 2
2.2%
41.66527 2
2.2%
41.66472 2
2.2%

submission_longitude
Real number (ℝ)

High correlation  Missing 

Distinct28
Distinct (%)39.4%
Missing22
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean-21.624542
Minimum-111.8634
Maximum121.76225
Zeros0
Zeros (%)0.0%
Negative39
Negative (%)41.9%
Memory size1.5 KiB
2025-07-07T16:07:52.655987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-111.8634
5-th percentile-93.296115
Q1-83.669675
median-81.94256
Q334.8064
95-th percentile121.76225
Maximum121.76225
Range233.62565
Interquartile range (IQR)118.47607

Descriptive statistics

Standard deviation76.036595
Coefficient of variation (CV)-3.5162176
Kurtosis-1.0463969
Mean-21.624542
Median Absolute Deviation (MAD)11.353555
Skewness0.59800139
Sum-1535.3425
Variance5781.5638
MonotonicityNot monotonic
2025-07-07T16:07:52.690170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
-93.296115 10
 
10.8%
34.8064 8
 
8.6%
-83.62744 6
 
6.5%
-83.62881 6
 
6.5%
121.762254 6
 
6.5%
29.58706 2
 
2.2%
-83.66485 2
 
2.2%
-71.57822 2
 
2.2%
15.242 2
 
2.2%
-83.63078 2
 
2.2%
Other values (18) 25
26.9%
(Missing) 22
23.7%
ValueCountFrequency (%)
-111.8634 1
 
1.1%
-97.9862 1
 
1.1%
-93.296115 10
10.8%
-83.675011 2
 
2.2%
-83.674547 2
 
2.2%
-83.6745 2
 
2.2%
-83.66485 2
 
2.2%
-83.63078 2
 
2.2%
-83.62881 6
6.5%
-83.62744 6
6.5%
ValueCountFrequency (%)
121.762254 6
6.5%
121.5327 1
 
1.1%
121.3063 1
 
1.1%
91.62693 1
 
1.1%
56.7133 1
 
1.1%
39.66955 2
 
2.2%
35.8267 1
 
1.1%
35.16159 1
 
1.1%
35.04128 1
 
1.1%
34.8064 8
8.6%

submission_elevation
Real number (ℝ)

High correlation  Missing 

Distinct27
Distinct (%)38.0%
Missing22
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean208.93662
Minimum3.6
Maximum1791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-07-07T16:07:52.721419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile9
Q151.2
median185.6
Q3202.8
95-th percentile474.85
Maximum1791
Range1787.4
Interquartile range (IQR)151.6

Descriptive statistics

Standard deviation274.59205
Coefficient of variation (CV)1.3142361
Kurtosis18.425411
Mean208.93662
Median Absolute Deviation (MAD)134.4
Skewness3.8008303
Sum14834.5
Variance75400.796
MonotonicityNot monotonic
2025-07-07T16:07:52.756966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
378 10
10.8%
192.5 10
10.8%
51.2 6
 
6.5%
185.6 6
 
6.5%
30.5 5
 
5.4%
30.4 3
 
3.2%
9 2
 
2.2%
192.1 2
 
2.2%
67.7 2
 
2.2%
189.7 2
 
2.2%
Other values (17) 23
24.7%
(Missing) 22
23.7%
ValueCountFrequency (%)
3.6 2
 
2.2%
8 1
 
1.1%
9 2
 
2.2%
11.3 1
 
1.1%
17.7 2
 
2.2%
30 1
 
1.1%
30.4 3
3.2%
30.5 5
5.4%
51.2 6
6.5%
66.2 2
 
2.2%
ValueCountFrequency (%)
1791 1
 
1.1%
1320.5 1
 
1.1%
621.4 1
 
1.1%
571.7 1
 
1.1%
378 10
10.8%
362.4 2
 
2.2%
327.4 1
 
1.1%
202.8 2
 
2.2%
192.5 10
10.8%
192.1 2
 
2.2%

submission_point
Categorical

High correlation  Missing 

Distinct29
Distinct (%)40.8%
Missing22
Missing (%)23.7%
Memory size9.9 KiB
01010000A0E6100000B936548CF35257C0D7FA22A12D2542400000000000A07740
10 
01010000A0E6100000B14F00C5C8705E402A8BC22E8AAE38409A99999999994940
01010000A0E6100000F20C1AFA27E854C07233DC80CFC744400000000000106840
01010000A0E610000075594C6C3EE854C0F9DA334B02C844403333333333336740
01010000A0E610000048BF7D1D3867414017B7D100DE0A40400000000000803E40
Other values (24)
38 

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters4686
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)15.5%

Sample

1st row01010000A0E610000073D712F241F75BC0F2D24D62106044400000000000A29440
2nd row01010000A0E6100000EF38454772792E40742497FF900E46400000000000002040
3rd row01010000A0E61000004F1E166A4D5B4C4061545227A05938409A99999999992640
4th row01010000A0E61000002ECA6C9049963D4066BD18CA89DE3E40CDCCCCCCCC8C5040
5th row01010000A0E61000002ECA6C9049963D4066BD18CA89DE3E40CDCCCCCCCC8C5040

Common Values

ValueCountFrequency (%)
01010000A0E6100000B936548CF35257C0D7FA22A12D2542400000000000A07740 10
 
10.8%
01010000A0E6100000B14F00C5C8705E402A8BC22E8AAE38409A99999999994940 6
 
6.5%
01010000A0E6100000F20C1AFA27E854C07233DC80CFC744400000000000106840 6
 
6.5%
01010000A0E610000075594C6C3EE854C0F9DA334B02C844403333333333336740 6
 
6.5%
01010000A0E610000048BF7D1D3867414017B7D100DE0A40400000000000803E40 5
 
5.4%
01010000A0E610000048BF7D1D38674140B459F5B9DA0A40406666666666663E40 3
 
3.2%
01010000A0E610000074417DCB9CEE1440AF42CA4FAA1D1D406666666666A67640 2
 
2.2%
01010000A0E6100000BA490C022BEB54C045813E9127D544400000000000106840 2
 
2.2%
01010000A0E610000023A12DE7527C54C014B4C9E1939A3A40CDCCCCCCCCCC0C40 2
 
2.2%
01010000A0E61000005B272EC72BEB54C0A27F828B15D544400000000000106840 2
 
2.2%
Other values (19) 27
29.0%
(Missing) 22
23.7%

Length

2025-07-07T16:07:52.794238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01010000a0e6100000b936548cf35257c0d7fa22a12d2542400000000000a07740 10
 
14.1%
01010000a0e6100000f20c1afa27e854c07233dc80cfc744400000000000106840 6
 
8.5%
01010000a0e610000075594c6c3ee854c0f9da334b02c844403333333333336740 6
 
8.5%
01010000a0e6100000b14f00c5c8705e402a8bc22e8aae38409a99999999994940 6
 
8.5%
01010000a0e610000048bf7d1d3867414017b7d100de0a40400000000000803e40 5
 
7.0%
01010000a0e610000048bf7d1d38674140b459f5b9da0a40406666666666663e40 3
 
4.2%
01010000a0e6100000b6847cd0b3d54340a7e8482eff6110c03333333333b33140 2
 
2.8%
01010000a0e610000023be13b35ee854c06fd8b628b3d544406666666666b66740 2
 
2.8%
01010000a0e610000096438b6ce77b2e4066f7e461a10e46400000000000002240 2
 
2.8%
01010000a0e61000002d5c566133eb54c0d15ad1e638d544403333333333036840 2
 
2.8%
Other values (19) 27
38.0%

Most occurring characters

ValueCountFrequency (%)
0 1552
33.1%
4 388
 
8.3%
1 346
 
7.4%
6 284
 
6.1%
3 257
 
5.5%
C 228
 
4.9%
A 218
 
4.7%
E 199
 
4.2%
9 199
 
4.2%
2 176
 
3.8%
Other values (6) 839
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1552
33.1%
4 388
 
8.3%
1 346
 
7.4%
6 284
 
6.1%
3 257
 
5.5%
C 228
 
4.9%
A 218
 
4.7%
E 199
 
4.2%
9 199
 
4.2%
2 176
 
3.8%
Other values (6) 839
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1552
33.1%
4 388
 
8.3%
1 346
 
7.4%
6 284
 
6.1%
3 257
 
5.5%
C 228
 
4.9%
A 218
 
4.7%
E 199
 
4.2%
9 199
 
4.2%
2 176
 
3.8%
Other values (6) 839
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1552
33.1%
4 388
 
8.3%
1 346
 
7.4%
6 284
 
6.1%
3 257
 
5.5%
C 228
 
4.9%
A 218
 
4.7%
E 199
 
4.2%
9 199
 
4.2%
2 176
 
3.8%
Other values (6) 839
17.9%

submission_data
Categorical

High correlation  Missing 

Distinct13
Distinct (%)30.2%
Missing50
Missing (%)53.8%
Memory size6.7 KiB
{'teacher_userid': 18536109}
12 
{'teacher_userid': 98766896}
{'teacher_userid': 8227960}
{'teacher_userid': 28365880}
{'teacher_userid': 21341588}
Other values (8)

Length

Max length29
Median length28
Mean length27.953488
Min length27

Characters and Unicode

Total characters1202
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)16.3%

Sample

1st row{'teacher_userid': 21341588}
2nd row{'teacher_userid': 113932171}
3rd row{'teacher_userid': 89134935}
4th row{'teacher_userid': 28365880}
5th row{'teacher_userid': 28365880}

Common Values

ValueCountFrequency (%)
{'teacher_userid': 18536109} 12
 
12.9%
{'teacher_userid': 98766896} 8
 
8.6%
{'teacher_userid': 8227960} 6
 
6.5%
{'teacher_userid': 28365880} 5
 
5.4%
{'teacher_userid': 21341588} 3
 
3.2%
{'teacher_userid': 137603655} 2
 
2.2%
{'teacher_userid': 113932171} 1
 
1.1%
{'teacher_userid': 89134935} 1
 
1.1%
{'teacher_userid': 37228303} 1
 
1.1%
{'teacher_userid': 86776301} 1
 
1.1%
Other values (3) 3
 
3.2%
(Missing) 50
53.8%

Length

2025-07-07T16:07:52.831508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
teacher_userid 43
50.0%
18536109 12
 
14.0%
98766896 8
 
9.3%
8227960 6
 
7.0%
28365880 5
 
5.8%
21341588 3
 
3.5%
137603655 2
 
2.3%
113932171 1
 
1.2%
89134935 1
 
1.2%
37228303 1
 
1.2%
Other values (4) 4
 
4.7%

Most occurring characters

ValueCountFrequency (%)
e 129
 
10.7%
r 86
 
7.2%
' 86
 
7.2%
8 62
 
5.2%
6 55
 
4.6%
{ 43
 
3.6%
i 43
 
3.6%
} 43
 
3.6%
43
 
3.6%
d 43
 
3.6%
Other values (16) 569
47.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 129
 
10.7%
r 86
 
7.2%
' 86
 
7.2%
8 62
 
5.2%
6 55
 
4.6%
{ 43
 
3.6%
i 43
 
3.6%
} 43
 
3.6%
43
 
3.6%
d 43
 
3.6%
Other values (16) 569
47.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 129
 
10.7%
r 86
 
7.2%
' 86
 
7.2%
8 62
 
5.2%
6 55
 
4.6%
{ 43
 
3.6%
i 43
 
3.6%
} 43
 
3.6%
43
 
3.6%
d 43
 
3.6%
Other values (16) 569
47.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 129
 
10.7%
r 86
 
7.2%
' 86
 
7.2%
8 62
 
5.2%
6 55
 
4.6%
{ 43
 
3.6%
i 43
 
3.6%
} 43
 
3.6%
43
 
3.6%
d 43
 
3.6%
Other values (16) 569
47.3%

protocol_set_name
Categorical

Constant  Missing 

Distinct1
Distinct (%)1.4%
Missing22
Missing (%)23.7%
Memory size6.6 KiB
Surface Temperature
71 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1349
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSurface Temperature
2nd rowSurface Temperature
3rd rowSurface Temperature
4th rowSurface Temperature
5th rowSurface Temperature

Common Values

ValueCountFrequency (%)
Surface Temperature 71
76.3%
(Missing) 22
 
23.7%

Length

2025-07-07T16:07:52.867771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:52.888861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surface 71
50.0%
temperature 71
50.0%

Most occurring characters

ValueCountFrequency (%)
e 284
21.1%
r 213
15.8%
u 142
10.5%
a 142
10.5%
S 71
 
5.3%
f 71
 
5.3%
c 71
 
5.3%
71
 
5.3%
T 71
 
5.3%
m 71
 
5.3%
Other values (2) 142
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 284
21.1%
r 213
15.8%
u 142
10.5%
a 142
10.5%
S 71
 
5.3%
f 71
 
5.3%
c 71
 
5.3%
71
 
5.3%
T 71
 
5.3%
m 71
 
5.3%
Other values (2) 142
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 284
21.1%
r 213
15.8%
u 142
10.5%
a 142
10.5%
S 71
 
5.3%
f 71
 
5.3%
c 71
 
5.3%
71
 
5.3%
T 71
 
5.3%
m 71
 
5.3%
Other values (2) 142
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 284
21.1%
r 213
15.8%
u 142
10.5%
a 142
10.5%
S 71
 
5.3%
f 71
 
5.3%
c 71
 
5.3%
71
 
5.3%
T 71
 
5.3%
m 71
 
5.3%
Other values (2) 142
10.5%

protocol_set_code
Categorical

Constant  Missing 

Distinct1
Distinct (%)1.4%
Missing22
Missing (%)23.7%
Memory size5.6 KiB
9808
71 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters284
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9808
2nd row9808
3rd row9808
4th row9808
5th row9808

Common Values

ValueCountFrequency (%)
9808 71
76.3%
(Missing) 22
 
23.7%

Length

2025-07-07T16:07:52.915020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:52.935379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9808 71
100.0%

Most occurring characters

ValueCountFrequency (%)
8 142
50.0%
9 71
25.0%
0 71
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 142
50.0%
9 71
25.0%
0 71
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 142
50.0%
9 71
25.0%
0 71
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 142
50.0%
9 71
25.0%
0 71
25.0%

site_name
Categorical

High correlation 

Distinct42
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
PLAYGROUND Weather Station - Alpena Elementary School
10 
Saint Rose Parking Lot
Turnip Cake
Saint Rose Grassy Hill
Hagefen astroturf 2025
 
5
Other values (37)
59 

Length

Max length53
Median length33
Mean length23.698925
Min length5

Characters and Unicode

Total characters2204
Distinct characters93
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)23.7%

Sample

1st rowDove Science Academy Concrete Temp.
2nd rowDove Science Academy Concrete Temp.
3rd rowDove Science Academy Concrete Temp.
4th rowالثانويه الثانيه بسكاكا
5th row12TVL271114

Common Values

ValueCountFrequency (%)
PLAYGROUND Weather Station - Alpena Elementary School 10
 
10.8%
Saint Rose Parking Lot 7
 
7.5%
Turnip Cake 6
 
6.5%
Saint Rose Grassy Hill 6
 
6.5%
Hagefen astroturf 2025 5
 
5.4%
Atmosphere 1 5
 
5.4%
Flint Hill Elementary School 4
 
4.3%
Hagefen concrete 2025 3
 
3.2%
Dove Science Academy Concrete Temp. 3
 
3.2%
Hawkins pathway to NSTC 2
 
2.2%
Other values (32) 42
45.2%

Length

2025-07-07T16:07:52.964439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
school 23
 
7.0%
elementary 14
 
4.3%
13
 
4.0%
saint 13
 
4.0%
rose 13
 
4.0%
playground 12
 
3.7%
station 11
 
3.4%
hill 10
 
3.0%
alpena 10
 
3.0%
parking 10
 
3.0%
Other values (84) 199
60.7%

Most occurring characters

ValueCountFrequency (%)
235
 
10.7%
a 166
 
7.5%
e 163
 
7.4%
o 121
 
5.5%
n 113
 
5.1%
t 110
 
5.0%
r 108
 
4.9%
i 102
 
4.6%
l 90
 
4.1%
S 66
 
3.0%
Other values (83) 930
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
235
 
10.7%
a 166
 
7.5%
e 163
 
7.4%
o 121
 
5.5%
n 113
 
5.1%
t 110
 
5.0%
r 108
 
4.9%
i 102
 
4.6%
l 90
 
4.1%
S 66
 
3.0%
Other values (83) 930
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
235
 
10.7%
a 166
 
7.5%
e 163
 
7.4%
o 121
 
5.5%
n 113
 
5.1%
t 110
 
5.0%
r 108
 
4.9%
i 102
 
4.6%
l 90
 
4.1%
S 66
 
3.0%
Other values (83) 930
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
235
 
10.7%
a 166
 
7.5%
e 163
 
7.4%
o 121
 
5.5%
n 113
 
5.1%
t 110
 
5.0%
r 108
 
4.9%
i 102
 
4.6%
l 90
 
4.1%
S 66
 
3.0%
Other values (83) 930
42.2%
Distinct37
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2014-08-25 00:00:00
Maximum2025-03-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T16:07:52.999692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:53.044227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)

site_deactivated_at
Unsupported

Missing  Rejected  Unsupported 

Missing93
Missing (%)100.0%
Memory size1.5 KiB

site_comments
Unsupported

Missing  Rejected  Unsupported 

Missing93
Missing (%)100.0%
Memory size5.8 KiB

site_latitude
Real number (ℝ)

High correlation 

Distinct41
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.127089
Minimum-32.623574
Maximum55.72422
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)3.2%
Memory size1.5 KiB
2025-07-07T16:07:53.088326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-32.623574
5-th percentile17.521648
Q132.0848
median36.290455
Q341.66472
95-th percentile49.327485
Maximum55.72422
Range88.347794
Interquartile range (IQR)9.57992

Descriptive statistics

Standard deviation12.330409
Coefficient of variation (CV)0.35102281
Kurtosis10.800615
Mean35.127089
Median Absolute Deviation (MAD)5.272115
Skewness-2.6521089
Sum3266.8192
Variance152.03899
MonotonicityNot monotonic
2025-07-07T16:07:53.128735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
36.290455 10
 
10.8%
41.56102 7
 
7.5%
24.681796 6
 
6.5%
41.56257 6
 
6.5%
32.0849 5
 
5.4%
49.327485 5
 
5.4%
38.8961 4
 
4.3%
32.0848 3
 
3.2%
36.155956 3
 
3.2%
7.27897 2
 
2.2%
Other values (31) 42
45.2%
ValueCountFrequency (%)
-32.623574 1
 
1.1%
-4.0957 2
 
2.2%
7.27897 2
 
2.2%
24.3501 1
 
1.1%
24.681796 6
6.5%
24.9752 1
 
1.1%
24.9788 1
 
1.1%
26.603819 2
 
2.2%
27.12228 1
 
1.1%
29.987889 1
 
1.1%
ValueCountFrequency (%)
55.72422 1
 
1.1%
53.1957 1
 
1.1%
49.327485 5
5.4%
44.1143 2
 
2.2%
44.1138 1
 
1.1%
42.8244 2
 
2.2%
41.762343 1
 
1.1%
41.761979 1
 
1.1%
41.71179 2
 
2.2%
41.66953 2
 
2.2%

site_longitude
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-25.801903
Minimum-111.8634
Maximum121.76225
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)55.9%
Memory size1.5 KiB
2025-07-07T16:07:53.166905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-111.8634
5-th percentile-95.887291
Q1-83.66485
median-77.2848
Q334.8064
95-th percentile121.76225
Maximum121.76225
Range233.62565
Interquartile range (IQR)118.47125

Descriptive statistics

Standard deviation71.976659
Coefficient of variation (CV)-2.7895873
Kurtosis-0.90377161
Mean-25.801903
Median Absolute Deviation (MAD)18.602491
Skewness0.63744125
Sum-2399.5769
Variance5180.6394
MonotonicityNot monotonic
2025-07-07T16:07:53.207884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
-93.296115 10
 
10.8%
34.8064 8
 
8.6%
-83.62744 7
 
7.5%
121.762254 6
 
6.5%
-83.62881 6
 
6.5%
31.156006 5
 
5.4%
-77.2848 4
 
4.3%
-95.887291 3
 
3.2%
-83.675011 2
 
2.2%
-83.674547 2
 
2.2%
Other values (30) 40
43.0%
ValueCountFrequency (%)
-111.8634 1
 
1.1%
-105.031741 1
 
1.1%
-97.9862 1
 
1.1%
-95.887291 3
 
3.2%
-93.296115 10
10.8%
-83.675011 2
 
2.2%
-83.674547 2
 
2.2%
-83.6745 2
 
2.2%
-83.66485 2
 
2.2%
-83.63078 2
 
2.2%
ValueCountFrequency (%)
121.762254 6
6.5%
121.5327 1
 
1.1%
121.3063 1
 
1.1%
91.62693 1
 
1.1%
56.7133 1
 
1.1%
40.212926 1
 
1.1%
39.66955 2
 
2.2%
35.8267 1
 
1.1%
35.16159 1
 
1.1%
35.04128 1
 
1.1%

site_elevation
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.19892
Minimum-5111.3
Maximum1791
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.1%
Memory size1.5 KiB
2025-07-07T16:07:53.245460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5111.3
5-th percentile9
Q151.2
median185.6
Q3202.8
95-th percentile575.1
Maximum1791
Range6902.3
Interquartile range (IQR)151.6

Descriptive statistics

Standard deviation622.76137
Coefficient of variation (CV)3.8874254
Kurtosis57.509282
Mean160.19892
Median Absolute Deviation (MAD)133.6
Skewness-6.308638
Sum14898.5
Variance387831.72
MonotonicityNot monotonic
2025-07-07T16:07:53.286142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
192.5 11
 
11.8%
378 10
 
10.8%
51.2 6
 
6.5%
185.6 6
 
6.5%
30.5 5
 
5.4%
141 5
 
5.4%
128.8 4
 
4.3%
30.4 3
 
3.2%
207.4 3
 
3.2%
362.4 2
 
2.2%
Other values (28) 38
40.9%
ValueCountFrequency (%)
-5111.3 1
 
1.1%
3.6 2
 
2.2%
8 1
 
1.1%
9 2
 
2.2%
11.3 1
 
1.1%
17.7 2
 
2.2%
30 1
 
1.1%
30.4 3
3.2%
30.5 5
5.4%
47.9 2
 
2.2%
ValueCountFrequency (%)
1791 1
 
1.1%
1617.9 1
 
1.1%
1320.5 1
 
1.1%
621.4 1
 
1.1%
580.2 1
 
1.1%
571.7 1
 
1.1%
440.8 1
 
1.1%
378 10
10.8%
362.4 2
 
2.2%
327.4 1
 
1.1%

site_elevation_type
Unsupported

Missing  Rejected  Unsupported 

Missing93
Missing (%)100.0%
Memory size5.8 KiB

site_location_source
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
gps
86 
other
 
7

Length

Max length5
Median length3
Mean length3.1505376
Min length3

Characters and Unicode

Total characters293
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother
2nd rowother
3rd rowother
4th rowgps
5th rowgps

Common Values

ValueCountFrequency (%)
gps 86
92.5%
other 7
 
7.5%

Length

2025-07-07T16:07:53.326605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:53.349955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gps 86
92.5%
other 7
 
7.5%

Most occurring characters

ValueCountFrequency (%)
g 86
29.4%
p 86
29.4%
s 86
29.4%
o 7
 
2.4%
t 7
 
2.4%
h 7
 
2.4%
e 7
 
2.4%
r 7
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
g 86
29.4%
p 86
29.4%
s 86
29.4%
o 7
 
2.4%
t 7
 
2.4%
h 7
 
2.4%
e 7
 
2.4%
r 7
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
g 86
29.4%
p 86
29.4%
s 86
29.4%
o 7
 
2.4%
t 7
 
2.4%
h 7
 
2.4%
e 7
 
2.4%
r 7
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
g 86
29.4%
p 86
29.4%
s 86
29.4%
o 7
 
2.4%
t 7
 
2.4%
h 7
 
2.4%
e 7
 
2.4%
r 7
 
2.4%

site_point
Categorical

High correlation 

Distinct41
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
01010000A0E6100000B936548CF35257C0D7FA22A12D2542400000000000A07740
10 
01010000A0E6100000F20C1AFA27E854C07233DC80CFC744400000000000106840
01010000A0E6100000B14F00C5C8705E402A8BC22E8AAE38409A99999999994940
01010000A0E610000075594C6C3EE854C0F9DA334B02C844403333333333336740
01010000A0E610000048BF7D1D3867414017B7D100DE0A40400000000000803E40
 
5
Other values (36)
59 

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters6138
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)21.5%

Sample

1st row01010000A0E610000040C23060C9F857C0B8CEBF5DF6134240CDCCCCCCCCEC6940
2nd row01010000A0E610000040C23060C9F857C0B8CEBF5DF6134240CDCCCCCCCCEC6940
3rd row01010000A0E610000040C23060C9F857C0B8CEBF5DF6134240CDCCCCCCCCEC6940
4th row01010000A0E6100000EB3BBF28411B44400114234BE6FC3D40CDCCCCCC4CF7B3C0
5th row01010000A0E610000073D712F241F75BC0F2D24D62106044400000000000A29440

Common Values

ValueCountFrequency (%)
01010000A0E6100000B936548CF35257C0D7FA22A12D2542400000000000A07740 10
 
10.8%
01010000A0E6100000F20C1AFA27E854C07233DC80CFC744400000000000106840 7
 
7.5%
01010000A0E6100000B14F00C5C8705E402A8BC22E8AAE38409A99999999994940 6
 
6.5%
01010000A0E610000075594C6C3EE854C0F9DA334B02C844403333333333336740 6
 
6.5%
01010000A0E610000048BF7D1D3867414017B7D100DE0A40400000000000803E40 5
 
5.4%
01010000A0E6100000D3FA5B02F0273F401D774A07EBA948400000000000A06140 5
 
5.4%
01010000A0E6100000A779C7293A5253C009F9A067B37243409A99999999196040 4
 
4.3%
01010000A0E610000048BF7D1D38674140B459F5B9DA0A40406666666666663E40 3
 
3.2%
01010000A0E610000040C23060C9F857C0B8CEBF5DF6134240CDCCCCCCCCEC6940 3
 
3.2%
01010000A0E61000002979758E01E551C044696FF085694540CDCCCCCCCCEC5040 2
 
2.2%
Other values (31) 42
45.2%

Length

2025-07-07T16:07:53.374147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01010000a0e6100000b936548cf35257c0d7fa22a12d2542400000000000a07740 10
 
10.8%
01010000a0e6100000f20c1afa27e854c07233dc80cfc744400000000000106840 7
 
7.5%
01010000a0e6100000b14f00c5c8705e402a8bc22e8aae38409a99999999994940 6
 
6.5%
01010000a0e610000075594c6c3ee854c0f9da334b02c844403333333333336740 6
 
6.5%
01010000a0e610000048bf7d1d3867414017b7d100de0a40400000000000803e40 5
 
5.4%
01010000a0e6100000d3fa5b02f0273f401d774a07eba948400000000000a06140 5
 
5.4%
01010000a0e6100000a779c7293a5253c009f9a067b37243409a99999999196040 4
 
4.3%
01010000a0e610000048bf7d1d38674140b459f5b9da0a40406666666666663e40 3
 
3.2%
01010000a0e610000040c23060c9f857c0b8cebf5df6134240cdccccccccec6940 3
 
3.2%
01010000a0e61000002d5c566133eb54c0d15ad1e638d544403333333333036840 2
 
2.2%
Other values (31) 42
45.2%

Most occurring characters

ValueCountFrequency (%)
0 2028
33.0%
4 503
 
8.2%
1 451
 
7.3%
6 342
 
5.6%
3 337
 
5.5%
C 318
 
5.2%
9 309
 
5.0%
A 289
 
4.7%
E 256
 
4.2%
7 226
 
3.7%
Other values (6) 1079
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2028
33.0%
4 503
 
8.2%
1 451
 
7.3%
6 342
 
5.6%
3 337
 
5.5%
C 318
 
5.2%
9 309
 
5.0%
A 289
 
4.7%
E 256
 
4.2%
7 226
 
3.7%
Other values (6) 1079
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2028
33.0%
4 503
 
8.2%
1 451
 
7.3%
6 342
 
5.6%
3 337
 
5.5%
C 318
 
5.2%
9 309
 
5.0%
A 289
 
4.7%
E 256
 
4.2%
7 226
 
3.7%
Other values (6) 1079
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2028
33.0%
4 503
 
8.2%
1 451
 
7.3%
6 342
 
5.6%
3 337
 
5.5%
C 318
 
5.2%
9 309
 
5.0%
A 289
 
4.7%
E 256
 
4.2%
7 226
 
3.7%
Other values (6) 1079
17.6%

site_developer_key_id
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
5
81 
1
 
8
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters93
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row1
5th row5

Common Values

ValueCountFrequency (%)
5 81
87.1%
1 8
 
8.6%
4 4
 
4.3%

Length

2025-07-07T16:07:53.402585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:53.423566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 81
87.1%
1 8
 
8.6%
4 4
 
4.3%

Most occurring characters

ValueCountFrequency (%)
5 81
87.1%
1 8
 
8.6%
4 4
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 81
87.1%
1 8
 
8.6%
4 4
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 81
87.1%
1 8
 
8.6%
4 4
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 81
87.1%
1 8
 
8.6%
4 4
 
4.3%

site_is_citizen_science
Boolean

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size930.0 B
False
93 
ValueCountFrequency (%)
False 93
100.0%
2025-07-07T16:07:53.438553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

site_nickname
Unsupported

Missing  Rejected  Unsupported 

Missing93
Missing (%)100.0%
Memory size5.8 KiB

site_true_latitude
Unsupported

Missing  Rejected  Unsupported 

Missing93
Missing (%)100.0%
Memory size1.5 KiB

site_true_longitude
Unsupported

Missing  Rejected  Unsupported 

Missing93
Missing (%)100.0%
Memory size1.5 KiB

site_true_elevation
Unsupported

Missing  Rejected  Unsupported 

Missing93
Missing (%)100.0%
Memory size1.5 KiB

site_true_point
Unsupported

Missing  Rejected  Unsupported 

Missing93
Missing (%)100.0%
Memory size1.5 KiB
Distinct6
Distinct (%)25.0%
Missing69
Missing (%)74.2%
Memory size1.5 KiB
Minimum2014-08-25 00:00:00
Maximum2021-08-17 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T16:07:53.456551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:53.484177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

site_photo_primary_thumb_url
Categorical

High correlation  Missing 

Distinct7
Distinct (%)29.2%
Missing69
Missing (%)74.2%
Memory size7.1 KiB
https://data.globe.gov/system/photos/2018/10/17/877470/thumb.jpg
10 
https://data.globe.gov/system/photos/2014/08/25/5818/thumb.jpg
https://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg
https://data.globe.gov/system/photos/2018/10/17/878207/thumb.jpg
 
1
https://data.globe.gov/system/photos/2020/10/04/1939848/thumb.jpg
 
1
Other values (2)

Length

Max length65
Median length64
Mean length63.625
Min length62

Characters and Unicode

Total characters1527
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)16.7%

Sample

1st rowhttps://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg
2nd rowhttps://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg
3rd rowhttps://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg
4th rowhttps://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg
5th rowhttps://data.globe.gov/system/photos/2018/10/17/877470/thumb.jpg

Common Values

ValueCountFrequency (%)
https://data.globe.gov/system/photos/2018/10/17/877470/thumb.jpg 10
 
10.8%
https://data.globe.gov/system/photos/2014/08/25/5818/thumb.jpg 6
 
6.5%
https://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg 4
 
4.3%
https://data.globe.gov/system/photos/2018/10/17/878207/thumb.jpg 1
 
1.1%
https://data.globe.gov/system/photos/2020/10/04/1939848/thumb.jpg 1
 
1.1%
https://data.globe.gov/system/photos/2021/08/17/2382687/thumb.jpg 1
 
1.1%
https://data.globe.gov/system/photos/2020/09/21/1908813/thumb.jpg 1
 
1.1%
(Missing) 69
74.2%

Length

2025-07-07T16:07:53.517092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:53.548559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
https://data.globe.gov/system/photos/2018/10/17/877470/thumb.jpg 10
41.7%
https://data.globe.gov/system/photos/2014/08/25/5818/thumb.jpg 6
25.0%
https://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg 4
 
16.7%
https://data.globe.gov/system/photos/2018/10/17/878207/thumb.jpg 1
 
4.2%
https://data.globe.gov/system/photos/2020/10/04/1939848/thumb.jpg 1
 
4.2%
https://data.globe.gov/system/photos/2021/08/17/2382687/thumb.jpg 1
 
4.2%
https://data.globe.gov/system/photos/2020/09/21/1908813/thumb.jpg 1
 
4.2%

Most occurring characters

ValueCountFrequency (%)
/ 216
 
14.1%
t 144
 
9.4%
s 96
 
6.3%
o 96
 
6.3%
h 72
 
4.7%
. 72
 
4.7%
g 72
 
4.7%
p 72
 
4.7%
1 68
 
4.5%
0 67
 
4.4%
Other values (19) 552
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1527
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 216
 
14.1%
t 144
 
9.4%
s 96
 
6.3%
o 96
 
6.3%
h 72
 
4.7%
. 72
 
4.7%
g 72
 
4.7%
p 72
 
4.7%
1 68
 
4.5%
0 67
 
4.4%
Other values (19) 552
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1527
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 216
 
14.1%
t 144
 
9.4%
s 96
 
6.3%
o 96
 
6.3%
h 72
 
4.7%
. 72
 
4.7%
g 72
 
4.7%
p 72
 
4.7%
1 68
 
4.5%
0 67
 
4.4%
Other values (19) 552
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1527
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 216
 
14.1%
t 144
 
9.4%
s 96
 
6.3%
o 96
 
6.3%
h 72
 
4.7%
. 72
 
4.7%
g 72
 
4.7%
p 72
 
4.7%
1 68
 
4.5%
0 67
 
4.4%
Other values (19) 552
36.1%

site_photo_primary_photo_url
Categorical

High correlation  Missing 

Distinct7
Distinct (%)29.2%
Missing69
Missing (%)74.2%
Memory size7.2 KiB
https://data.globe.gov/system/photos/2018/10/17/877470/original.jpg
10 
https://data.globe.gov/system/photos/2014/08/25/5818/original.jpg
https://data.globe.gov/system/photos/2017/01/25/121075/original.jpg
https://data.globe.gov/system/photos/2018/10/17/878207/original.jpg
 
1
https://data.globe.gov/system/photos/2020/10/04/1939848/original.jpeg
 
1
Other values (2)

Length

Max length69
Median length67
Mean length66.666667
Min length65

Characters and Unicode

Total characters1600
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)16.7%

Sample

1st rowhttps://data.globe.gov/system/photos/2017/01/25/121075/original.jpg
2nd rowhttps://data.globe.gov/system/photos/2017/01/25/121075/original.jpg
3rd rowhttps://data.globe.gov/system/photos/2017/01/25/121075/original.jpg
4th rowhttps://data.globe.gov/system/photos/2017/01/25/121075/original.jpg
5th rowhttps://data.globe.gov/system/photos/2018/10/17/877470/original.jpg

Common Values

ValueCountFrequency (%)
https://data.globe.gov/system/photos/2018/10/17/877470/original.jpg 10
 
10.8%
https://data.globe.gov/system/photos/2014/08/25/5818/original.jpg 6
 
6.5%
https://data.globe.gov/system/photos/2017/01/25/121075/original.jpg 4
 
4.3%
https://data.globe.gov/system/photos/2018/10/17/878207/original.jpg 1
 
1.1%
https://data.globe.gov/system/photos/2020/10/04/1939848/original.jpeg 1
 
1.1%
https://data.globe.gov/system/photos/2021/08/17/2382687/original.jpg 1
 
1.1%
https://data.globe.gov/system/photos/2020/09/21/1908813/original.jpg 1
 
1.1%
(Missing) 69
74.2%

Length

2025-07-07T16:07:53.604921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:53.637243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
https://data.globe.gov/system/photos/2018/10/17/877470/original.jpg 10
41.7%
https://data.globe.gov/system/photos/2014/08/25/5818/original.jpg 6
25.0%
https://data.globe.gov/system/photos/2017/01/25/121075/original.jpg 4
 
16.7%
https://data.globe.gov/system/photos/2018/10/17/878207/original.jpg 1
 
4.2%
https://data.globe.gov/system/photos/2020/10/04/1939848/original.jpeg 1
 
4.2%
https://data.globe.gov/system/photos/2021/08/17/2382687/original.jpg 1
 
4.2%
https://data.globe.gov/system/photos/2020/09/21/1908813/original.jpg 1
 
4.2%

Most occurring characters

ValueCountFrequency (%)
/ 216
 
13.5%
t 120
 
7.5%
o 120
 
7.5%
s 96
 
6.0%
g 96
 
6.0%
p 72
 
4.5%
a 72
 
4.5%
. 72
 
4.5%
1 68
 
4.2%
0 67
 
4.2%
Other values (21) 601
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 216
 
13.5%
t 120
 
7.5%
o 120
 
7.5%
s 96
 
6.0%
g 96
 
6.0%
p 72
 
4.5%
a 72
 
4.5%
. 72
 
4.5%
1 68
 
4.2%
0 67
 
4.2%
Other values (21) 601
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 216
 
13.5%
t 120
 
7.5%
o 120
 
7.5%
s 96
 
6.0%
g 96
 
6.0%
p 72
 
4.5%
a 72
 
4.5%
. 72
 
4.5%
1 68
 
4.2%
0 67
 
4.2%
Other values (21) 601
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 216
 
13.5%
t 120
 
7.5%
o 120
 
7.5%
s 96
 
6.0%
g 96
 
6.0%
p 72
 
4.5%
a 72
 
4.5%
. 72
 
4.5%
1 68
 
4.2%
0 67
 
4.2%
Other values (21) 601
37.6%

site_photo_photo_data
Categorical

High correlation  Missing 

Distinct7
Distinct (%)29.2%
Missing69
Missing (%)74.2%
Memory size48.5 KiB
{'North': [{'photo_id': 877470, 'date_taken': '2018-10-17', 'caption': 'View NRTH from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877470/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877470/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877470/thumb.jpg', 'created_at': '2018-10-17T23:02:00.666494'}], 'East': [{'photo_id': 877471, 'date_taken': '2018-10-17', 'caption': 'View EAST from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877471/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877471/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877471/thumb.jpg', 'created_at': '2018-10-17T23:02:24.672352'}], 'South': [{'photo_id': 877472, 'date_taken': '2018-10-17', 'caption': 'View SOUTH from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877472/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877472/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877472/thumb.jpg', 'created_at': '2018-10-17T23:02:41.897805'}], 'West': [{'photo_id': 877473, 'date_taken': '2018-10-17', 'caption': 'View West from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877473/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877473/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877473/thumb.jpg', 'created_at': '2018-10-17T23:03:00.566186'}], 'Upward': [{'photo_id': 877474, 'date_taken': '2018-10-17', 'caption': 'View UP', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877474/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877474/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877474/thumb.jpg', 'created_at': '2018-10-17T23:08:56.653446'}], 'Downward': [{'photo_id': 877475, 'date_taken': '2018-10-17', 'caption': 'View DOWN', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877475/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877475/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877475/thumb.jpg', 'created_at': '2018-10-17T23:09:21.367125'}]}
10 
{'North': [{'photo_id': 5818, 'date_taken': '2014-08-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2014/08/25/5818/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2014/08/25/5818/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2014/08/25/5818/thumb.jpg', 'created_at': '2014-08-25T07:52:15.433179'}], 'East': [{'photo_id': 5819, 'date_taken': '2014-08-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2014/08/25/5819/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2014/08/25/5819/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2014/08/25/5819/thumb.jpg', 'created_at': '2014-08-25T07:52:57.662085'}], 'South': [{'photo_id': 5820, 'date_taken': '2014-08-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2014/08/25/5820/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2014/08/25/5820/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2014/08/25/5820/thumb.jpg', 'created_at': '2014-08-25T07:53:22.405523'}]}
{'East': [{'photo_id': 121075, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg', 'created_at': '2017-01-25T19:07:09.847289'}], 'South': [{'photo_id': 121076, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/thumb.jpg', 'created_at': '2017-01-25T19:07:30.123722'}], 'West': [{'photo_id': 121077, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/thumb.jpg', 'created_at': '2017-01-25T19:07:57.013095'}]}
{'West': [{'photo_id': 878207, 'date_taken': '2018-10-17', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/878207/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/878207/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/878207/thumb.jpg', 'created_at': '2018-10-18T15:50:50.047083'}]}
 
1
{'North': [{'photo_id': 1939848, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1939848/original.jpeg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1939848/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1939848/thumb.jpg', 'created_at': '2020-10-04T13:33:07.873575'}], 'East': [{'photo_id': 1939849, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1939849/original.jpeg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1939849/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1939849/thumb.jpg', 'created_at': '2020-10-04T13:33:44.813678'}], 'South': [{'photo_id': 1939850, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1939850/original.jpeg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1939850/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1939850/thumb.jpg', 'created_at': '2020-10-04T13:34:30.432983'}], 'West': [{'photo_id': 1939852, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1939852/original.jpeg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1939852/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1939852/thumb.jpg', 'created_at': '2020-10-04T13:35:28.987119'}], 'Upward': [{'photo_id': 1957370, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1957370/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1957370/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1957370/thumb.jpg', 'created_at': '2020-10-12T21:00:37.506477'}], 'Downward': [{'photo_id': 1957373, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1957373/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1957373/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1957373/thumb.jpg', 'created_at': '2020-10-12T21:00:47.475082'}]}
 
1
Other values (2)

Length

Max length2521
Median length2424
Mean length1828
Min length398

Characters and Unicode

Total characters43872
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)16.7%

Sample

1st row{'East': [{'photo_id': 121075, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg', 'created_at': '2017-01-25T19:07:09.847289'}], 'South': [{'photo_id': 121076, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/thumb.jpg', 'created_at': '2017-01-25T19:07:30.123722'}], 'West': [{'photo_id': 121077, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/thumb.jpg', 'created_at': '2017-01-25T19:07:57.013095'}]}
2nd row{'East': [{'photo_id': 121075, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg', 'created_at': '2017-01-25T19:07:09.847289'}], 'South': [{'photo_id': 121076, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/thumb.jpg', 'created_at': '2017-01-25T19:07:30.123722'}], 'West': [{'photo_id': 121077, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/thumb.jpg', 'created_at': '2017-01-25T19:07:57.013095'}]}
3rd row{'East': [{'photo_id': 121075, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg', 'created_at': '2017-01-25T19:07:09.847289'}], 'South': [{'photo_id': 121076, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/thumb.jpg', 'created_at': '2017-01-25T19:07:30.123722'}], 'West': [{'photo_id': 121077, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/thumb.jpg', 'created_at': '2017-01-25T19:07:57.013095'}]}
4th row{'East': [{'photo_id': 121075, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg', 'created_at': '2017-01-25T19:07:09.847289'}], 'South': [{'photo_id': 121076, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/thumb.jpg', 'created_at': '2017-01-25T19:07:30.123722'}], 'West': [{'photo_id': 121077, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/thumb.jpg', 'created_at': '2017-01-25T19:07:57.013095'}]}
5th row{'North': [{'photo_id': 877470, 'date_taken': '2018-10-17', 'caption': 'View NRTH from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877470/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877470/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877470/thumb.jpg', 'created_at': '2018-10-17T23:02:00.666494'}], 'East': [{'photo_id': 877471, 'date_taken': '2018-10-17', 'caption': 'View EAST from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877471/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877471/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877471/thumb.jpg', 'created_at': '2018-10-17T23:02:24.672352'}], 'South': [{'photo_id': 877472, 'date_taken': '2018-10-17', 'caption': 'View SOUTH from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877472/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877472/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877472/thumb.jpg', 'created_at': '2018-10-17T23:02:41.897805'}], 'West': [{'photo_id': 877473, 'date_taken': '2018-10-17', 'caption': 'View West from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877473/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877473/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877473/thumb.jpg', 'created_at': '2018-10-17T23:03:00.566186'}], 'Upward': [{'photo_id': 877474, 'date_taken': '2018-10-17', 'caption': 'View UP', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877474/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877474/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877474/thumb.jpg', 'created_at': '2018-10-17T23:08:56.653446'}], 'Downward': [{'photo_id': 877475, 'date_taken': '2018-10-17', 'caption': 'View DOWN', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877475/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877475/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877475/thumb.jpg', 'created_at': '2018-10-17T23:09:21.367125'}]}

Common Values

ValueCountFrequency (%)
{'North': [{'photo_id': 877470, 'date_taken': '2018-10-17', 'caption': 'View NRTH from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877470/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877470/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877470/thumb.jpg', 'created_at': '2018-10-17T23:02:00.666494'}], 'East': [{'photo_id': 877471, 'date_taken': '2018-10-17', 'caption': 'View EAST from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877471/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877471/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877471/thumb.jpg', 'created_at': '2018-10-17T23:02:24.672352'}], 'South': [{'photo_id': 877472, 'date_taken': '2018-10-17', 'caption': 'View SOUTH from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877472/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877472/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877472/thumb.jpg', 'created_at': '2018-10-17T23:02:41.897805'}], 'West': [{'photo_id': 877473, 'date_taken': '2018-10-17', 'caption': 'View West from Weather Station', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877473/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877473/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877473/thumb.jpg', 'created_at': '2018-10-17T23:03:00.566186'}], 'Upward': [{'photo_id': 877474, 'date_taken': '2018-10-17', 'caption': 'View UP', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877474/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877474/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877474/thumb.jpg', 'created_at': '2018-10-17T23:08:56.653446'}], 'Downward': [{'photo_id': 877475, 'date_taken': '2018-10-17', 'caption': 'View DOWN', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/877475/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/877475/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/877475/thumb.jpg', 'created_at': '2018-10-17T23:09:21.367125'}]} 10
 
10.8%
{'North': [{'photo_id': 5818, 'date_taken': '2014-08-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2014/08/25/5818/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2014/08/25/5818/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2014/08/25/5818/thumb.jpg', 'created_at': '2014-08-25T07:52:15.433179'}], 'East': [{'photo_id': 5819, 'date_taken': '2014-08-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2014/08/25/5819/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2014/08/25/5819/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2014/08/25/5819/thumb.jpg', 'created_at': '2014-08-25T07:52:57.662085'}], 'South': [{'photo_id': 5820, 'date_taken': '2014-08-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2014/08/25/5820/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2014/08/25/5820/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2014/08/25/5820/thumb.jpg', 'created_at': '2014-08-25T07:53:22.405523'}]} 6
 
6.5%
{'East': [{'photo_id': 121075, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121075/thumb.jpg', 'created_at': '2017-01-25T19:07:09.847289'}], 'South': [{'photo_id': 121076, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121076/thumb.jpg', 'created_at': '2017-01-25T19:07:30.123722'}], 'West': [{'photo_id': 121077, 'date_taken': '2017-01-25', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2017/01/25/121077/thumb.jpg', 'created_at': '2017-01-25T19:07:57.013095'}]} 4
 
4.3%
{'West': [{'photo_id': 878207, 'date_taken': '2018-10-17', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2018/10/17/878207/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2018/10/17/878207/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2018/10/17/878207/thumb.jpg', 'created_at': '2018-10-18T15:50:50.047083'}]} 1
 
1.1%
{'North': [{'photo_id': 1939848, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1939848/original.jpeg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1939848/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1939848/thumb.jpg', 'created_at': '2020-10-04T13:33:07.873575'}], 'East': [{'photo_id': 1939849, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1939849/original.jpeg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1939849/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1939849/thumb.jpg', 'created_at': '2020-10-04T13:33:44.813678'}], 'South': [{'photo_id': 1939850, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1939850/original.jpeg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1939850/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1939850/thumb.jpg', 'created_at': '2020-10-04T13:34:30.432983'}], 'West': [{'photo_id': 1939852, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1939852/original.jpeg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1939852/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1939852/thumb.jpg', 'created_at': '2020-10-04T13:35:28.987119'}], 'Upward': [{'photo_id': 1957370, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1957370/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1957370/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1957370/thumb.jpg', 'created_at': '2020-10-12T21:00:37.506477'}], 'Downward': [{'photo_id': 1957373, 'date_taken': '2020-10-04', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/10/04/1957373/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2020/10/04/1957373/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/10/04/1957373/thumb.jpg', 'created_at': '2020-10-12T21:00:47.475082'}]} 1
 
1.1%
{'North': [{'photo_id': 2382687, 'date_taken': '2021-08-17', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2021/08/17/2382687/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2021/08/17/2382687/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2021/08/17/2382687/thumb.jpg', 'created_at': '2021-08-17T15:26:55.912502'}], 'East': [{'photo_id': 2382688, 'date_taken': '2021-08-17', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2021/08/17/2382688/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2021/08/17/2382688/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2021/08/17/2382688/thumb.jpg', 'created_at': '2021-08-17T15:28:57.45266'}], 'South': [{'photo_id': 2382689, 'date_taken': '2021-08-17', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2021/08/17/2382689/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2021/08/17/2382689/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2021/08/17/2382689/thumb.jpg', 'created_at': '2021-08-17T15:29:54.506737'}], 'West': [{'photo_id': 2382690, 'date_taken': '2021-08-17', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2021/08/17/2382690/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2021/08/17/2382690/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2021/08/17/2382690/thumb.jpg', 'created_at': '2021-08-17T15:31:24.29385'}]} 1
 
1.1%
{'North': [{'photo_id': 1908813, 'date_taken': '2020-09-21', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/09/21/1908813/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2020/09/21/1908813/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/09/21/1908813/thumb.jpg', 'created_at': '2020-09-21T20:20:43.008859'}], 'East': [{'photo_id': 1908814, 'date_taken': '2020-09-21', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/09/21/1908814/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2020/09/21/1908814/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/09/21/1908814/thumb.jpg', 'created_at': '2020-09-21T20:20:52.364697'}], 'South': [{'photo_id': 1908815, 'date_taken': '2020-09-21', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/09/21/1908815/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2020/09/21/1908815/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/09/21/1908815/thumb.jpg', 'created_at': '2020-09-21T20:21:01.147195'}], 'West': [{'photo_id': 1908816, 'date_taken': '2020-09-21', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/09/21/1908816/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2020/09/21/1908816/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/09/21/1908816/thumb.jpg', 'created_at': '2020-09-21T20:21:10.361037'}], 'Upward': [{'photo_id': 1908817, 'date_taken': '2020-09-21', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/09/21/1908817/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2020/09/21/1908817/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/09/21/1908817/thumb.jpg', 'created_at': '2020-09-21T20:21:19.230688'}], 'Downward': [{'photo_id': 1908818, 'date_taken': '2020-09-21', 'caption': None, 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2020/09/21/1908818/original.jpg', 'small_url': 'https://data.globe.gov/system/photos/2020/09/21/1908818/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2020/09/21/1908818/thumb.jpg', 'created_at': '2020-09-21T20:21:28.683789'}]} 1
 
1.1%
(Missing) 69
74.2%

Length

2025-07-07T16:07:53.693100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:53.803635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
thumb_url 107
 
5.4%
date_taken 107
 
5.4%
caption 107
 
5.4%
photo_id 107
 
5.4%
approval_status 107
 
5.4%
approved 107
 
5.4%
image_url 107
 
5.4%
created_at 107
 
5.4%
small_url 107
 
5.4%
2018-10-17 61
 
3.1%
Other values (164) 975
48.8%

Most occurring characters

ValueCountFrequency (%)
' 3330
 
7.6%
/ 2889
 
6.6%
t 2888
 
6.6%
a 2160
 
4.9%
o 2107
 
4.8%
1975
 
4.5%
s 1763
 
4.0%
p 1617
 
3.7%
1 1581
 
3.6%
0 1506
 
3.4%
Other values (48) 22056
50.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 3330
 
7.6%
/ 2889
 
6.6%
t 2888
 
6.6%
a 2160
 
4.9%
o 2107
 
4.8%
1975
 
4.5%
s 1763
 
4.0%
p 1617
 
3.7%
1 1581
 
3.6%
0 1506
 
3.4%
Other values (48) 22056
50.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 3330
 
7.6%
/ 2889
 
6.6%
t 2888
 
6.6%
a 2160
 
4.9%
o 2107
 
4.8%
1975
 
4.5%
s 1763
 
4.0%
p 1617
 
3.7%
1 1581
 
3.6%
0 1506
 
3.4%
Other values (48) 22056
50.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 3330
 
7.6%
/ 2889
 
6.6%
t 2888
 
6.6%
a 2160
 
4.9%
o 2107
 
4.8%
1975
 
4.5%
s 1763
 
4.0%
p 1617
 
3.7%
1 1581
 
3.6%
0 1506
 
3.4%
Other values (48) 22056
50.3%

developer_key_name
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
GLOBE Observer App
81 
GLOBE Data Entry Web Forms
 
8
GLOBE Data Entry App
 
4

Length

Max length26
Median length18
Mean length18.774194
Min length18

Characters and Unicode

Total characters1746
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLOBE Data Entry App
2nd rowGLOBE Data Entry App
3rd rowGLOBE Data Entry App
4th rowGLOBE Data Entry Web Forms
5th rowGLOBE Observer App

Common Values

ValueCountFrequency (%)
GLOBE Observer App 81
87.1%
GLOBE Data Entry Web Forms 8
 
8.6%
GLOBE Data Entry App 4
 
4.3%

Length

2025-07-07T16:07:54.311104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T16:07:54.335389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
globe 93
31.1%
app 85
28.4%
observer 81
27.1%
data 12
 
4.0%
entry 12
 
4.0%
web 8
 
2.7%
forms 8
 
2.7%

Most occurring characters

ValueCountFrequency (%)
206
11.8%
r 182
10.4%
O 174
10.0%
e 170
9.7%
p 170
9.7%
E 105
 
6.0%
G 93
 
5.3%
L 93
 
5.3%
B 93
 
5.3%
b 89
 
5.1%
Other values (12) 371
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
206
11.8%
r 182
10.4%
O 174
10.0%
e 170
9.7%
p 170
9.7%
E 105
 
6.0%
G 93
 
5.3%
L 93
 
5.3%
B 93
 
5.3%
b 89
 
5.1%
Other values (12) 371
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
206
11.8%
r 182
10.4%
O 174
10.0%
e 170
9.7%
p 170
9.7%
E 105
 
6.0%
G 93
 
5.3%
L 93
 
5.3%
B 93
 
5.3%
b 89
 
5.1%
Other values (12) 371
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
206
11.8%
r 182
10.4%
O 174
10.0%
e 170
9.7%
p 170
9.7%
E 105
 
6.0%
G 93
 
5.3%
L 93
 
5.3%
B 93
 
5.3%
b 89
 
5.1%
Other values (12) 371
21.2%

developer_key_is_citizen_science
Boolean

High correlation 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size930.0 B
True
81 
False
12 
ValueCountFrequency (%)
True 81
87.1%
False 12
 
12.9%
2025-07-07T16:07:54.357019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

PCA_outlier_flag
Boolean

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size837.0 B
True
93 
ValueCountFrequency (%)
True 93
100.0%
2025-07-07T16:07:54.370678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

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2025-07-07T16:07:41.145127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:41.808306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:42.318255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:42.806103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:46.107159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:46.575547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:47.529650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:48.004113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:43.514474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:43.541663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:44.049620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:45.010146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:45.489690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:48.052989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:48.746873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:47.610785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:48.077878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:48.771391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:49.293570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:41.277025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:42.455531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:44.101602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:45.064283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:41.305641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:42.959628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:43.624169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:44.129992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:44.599184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:45.093024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:45.763418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:46.231660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:46.703230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:47.665679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:48.129367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:48.822240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:49.348999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:40.348612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:40.844587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:41.330416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:42.001868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:42.983367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:46.255639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T16:07:41.356526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:42.028454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:42.539634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:43.006992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:43.677713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:44.182559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:44.647353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:45.144222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:45.812145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:46.279594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:46.752751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:47.216361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:47.719106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:48.175031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T16:07:48.868870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-07T16:07:54.405823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
developer_key_is_citizen_sciencedeveloper_key_namehomogeneous_site_long_length_mhomogeneous_site_short_length_minstrument_typeorganizationidsample_numbersample_surface_temperature_csite_developer_key_idsite_elevationsite_idsite_latitudesite_location_sourcesite_longitudesite_namesite_photo_photo_datasite_photo_primary_photo_urlsite_photo_primary_thumb_urlsite_pointst_idsts_idsubmission_datasubmission_elevationsubmission_idsubmission_latitudesubmission_longitudesubmission_pointsurface_conditionsurface_cover_typeuser_type_descriptionuseridusertypeversionversion_id
developer_key_is_citizen_science1.0000.9940.2600.2480.8120.4230.0000.4290.9940.2450.6190.0000.6760.0550.7490.8790.8790.8790.7560.9780.9781.0001.0001.0001.0001.0001.0000.0000.4920.4410.6200.4410.0000.961
developer_key_name0.9941.0000.1000.1890.8210.4100.0000.2891.0000.2520.6250.0000.8340.2720.7530.9000.9000.9000.7600.7360.7701.0001.0001.0001.0001.0001.0000.0680.5680.5120.3950.5120.0000.650
homogeneous_site_long_length_m0.2600.1001.0000.9540.637-0.2930.092-0.2400.1000.575-0.2590.2160.148-0.6370.7750.9000.9000.9000.783-0.693-0.6930.7850.529-0.6670.229-0.6030.8060.0000.6270.145-0.5660.145-0.124-0.768
homogeneous_site_short_length_m0.2480.1890.9541.0000.507-0.1730.041-0.2290.1890.605-0.1500.3220.185-0.7110.7750.9000.9000.9000.783-0.680-0.6800.8260.585-0.6650.337-0.7040.8130.0950.4750.212-0.5830.212-0.182-0.749
instrument_type0.8120.8210.6370.5071.0000.6990.3120.3510.8210.8210.7230.6980.8120.6560.8831.0001.0001.0000.8490.5430.5730.6240.8760.6550.6090.6730.7910.6260.7930.5260.7700.5260.0000.679
organizationid0.4230.410-0.293-0.1730.6991.000-0.2910.2690.410-0.2530.7420.1860.3440.1220.7840.9000.9000.9000.7920.3270.3270.838-0.1830.1710.1680.0510.8300.2950.4540.4030.4620.403-0.2120.379
sample_number0.0000.0000.0920.0410.312-0.2911.0000.0700.0000.012-0.247-0.1250.106-0.1010.2470.0000.0000.0000.236-0.022-0.0220.0000.028-0.048-0.085-0.1460.1370.2550.0000.000-0.0280.000-0.037-0.008
sample_surface_temperature_c0.4290.289-0.240-0.2290.3510.2690.0701.0000.289-0.3380.339-0.4930.4400.3080.4170.4550.4550.4550.4310.2260.2260.236-0.223-0.076-0.4600.2650.3350.1630.2500.2070.4650.207-0.0200.257
site_developer_key_id0.9941.0000.1000.1890.8210.4100.0000.2891.0000.2520.6250.0000.8340.2720.7530.9000.9000.9000.7600.7360.7701.0001.0001.0001.0001.0001.0000.0680.5680.5120.3950.5120.0000.650
site_elevation0.2450.2520.5750.6050.821-0.2530.012-0.3380.2521.000-0.0900.2170.054-0.6550.7570.9000.9000.9000.764-0.398-0.3980.8661.000-0.4230.199-0.6490.7860.3970.4290.000-0.5050.000-0.060-0.418
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submission_point1.0001.0000.8060.8130.7910.8300.1370.3351.0000.7860.8100.8041.0000.8160.9880.8560.8560.8561.0000.6840.6840.8940.8040.6080.8160.8161.0000.6050.7970.6920.7690.6920.5130.563
surface_condition0.0000.0680.0000.0950.6260.2950.2550.1630.0680.3970.3110.0000.1510.1780.5820.0000.0000.0000.5920.7140.7080.3100.7190.7140.0000.1920.6051.0000.0970.1430.4870.1430.0000.677
surface_cover_type0.4920.5680.6270.4750.7930.4540.0000.2500.5680.4290.4380.4800.6390.4120.7730.8160.8160.8160.7780.2040.2190.6510.7110.1570.4180.4610.7970.0971.0000.5550.3600.5550.0000.307
user_type_description0.4410.5120.1450.2120.5260.4030.0000.2070.5120.0000.2990.3190.5160.3210.5830.8790.8790.8790.5920.4180.4891.0000.5390.5600.4550.4840.6920.1430.5551.0000.3851.0000.0000.399
userid0.6200.395-0.566-0.5830.7700.462-0.0280.4650.395-0.5050.526-0.1520.3900.4720.7550.8300.8300.8300.7630.6400.6400.760-0.5010.500-0.1770.4710.7690.4870.3600.3851.0000.385-0.1580.673
usertype0.4410.5120.1450.2120.5260.4030.0000.2070.5120.0000.2990.3190.5160.3210.5830.8790.8790.8790.5920.4180.4891.0000.5390.5600.4550.4840.6920.1430.5551.0000.3851.0000.0000.399
version0.0000.000-0.124-0.1820.000-0.212-0.037-0.0200.000-0.060-0.286-0.1520.0000.2810.3430.5380.5380.5380.3590.2740.2740.5460.0330.073-0.1940.2270.5130.0000.0000.000-0.1580.0001.0000.203
version_id0.9610.650-0.768-0.7490.6790.379-0.0080.2570.650-0.4180.479-0.1190.6840.5020.6670.5770.5770.5770.5660.9410.9410.249-0.4770.907-0.0710.5250.5630.6770.3070.3990.6730.3990.2031.000

Missing values

2025-07-07T16:07:49.472361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-07T16:07:49.621395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-07T16:07:49.780243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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20492426431037752018-02-16 09:49:00811763800None11763857-1<NA>2018-02-16 15:54:49.2168352018-02-16 15:54:49.21683528428014.40NaN<NA>1136412017-12-22 22:34:49.5285352017-12-22 22:34:49.528525Concrete Surface90.090.0concreteBenetech GM3200Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Dove Science Academy Concrete Temp.2017-12-22NaT<NA>36.155956-95.887291207.4<NA>other01010000A0E610000040C23060C9F857C0B8CEBF5DF6134240CDCCCCCCCCEC69404False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry AppFalseTrue
20539435871037752018-03-06 09:53:00811763800None11763857-1<NA>2018-03-06 15:55:27.2036762018-03-06 15:55:27.20367628732518.90NaN<NA>1156322018-02-26 21:12:21.1408372018-02-26 21:12:21.140829Concrete Surface90.090.0concreteBenetech GM3200Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Dove Science Academy Concrete Temp.2017-12-22NaT<NA>36.155956-95.887291207.4<NA>other01010000A0E610000040C23060C9F857C0B8CEBF5DF6134240CDCCCCCCCCEC69404False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry AppFalseTrue
21967561031037752019-02-27 18:35:00811763800wet1176385711<NA>2019-02-27 18:38:03.7349932019-02-27 18:38:03.7349933521281-1.25NaN<NA>1156322018-02-26 21:12:21.1408372018-02-26 21:12:21.140829Concrete Surface90.090.0concreteBenetech GM3200Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenon-student user - trained<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Dove Science Academy Concrete Temp.2017-12-22NaT<NA>36.155956-95.887291207.4<NA>other01010000A0E610000040C23060C9F857C0B8CEBF5DF6134240CDCCCCCCCCEC69404False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry AppFalseTrue
23793743131020602019-10-16 09:17:00831943308dry3194187811<NA>2019-12-31 15:39:12.2606952019-12-31 15:39:12.260695420889136.00NaN<NA>1165422018-03-20 08:43:35.5690372018-03-20 08:43:35.569033<NA>NaNNaN<NA><NA>Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenon-student user - trained<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>الثانويه الثانيه بسكاكا2017-11-12NaT<NA>29.98788940.212926-5111.3<NA>gps01010000A0E6100000EB3BBF28411B44400114234BE6FC3D40CDCCCCCC4CF7B3C01False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalseTrue
341761300772689842023-03-27 19:19:00868138946snow6813939311420575332023-03-27 19:26:51.4842702023-03-27 19:26:51.4842706062733-2.4050.0measurable2160212022-03-29 21:17:21.9076732022-03-29 21:17:21.907673auto-sync from atmosphere_sites90.090.0short grassEtekcity 749Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenon-student user - trainedThe snow was slightly warmer, the closer it was to the parking lot.5<NA>40.75050-111.863401320.501010000A0E610000073D712F241F75BC0F2D24D62106044400000000000A29440NoneSurface Temperature980812TVL2711142022-03-02NaT<NA>40.750500-111.8634001320.5<NA>gps01010000A0E610000073D712F241F75BC0F2D24D62106044400000000000A294405False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Observer AppTrueTrue
383731650752181322025-03-07 09:11:008116319735dry6984168321591446882025-03-07 09:12:44.4564052025-03-07 09:12:44.45640572275737.00NaN<NA>1010572272025-03-05 10:01:32.0446612025-03-05 10:01:32.044647<NA>5.05.0asphalt<NA>Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherestudent user - trained<NA>5<NA>44.1138015.237208.001010000A0E6100000EF38454772792E40742497FF900E46400000000000002040{'teacher_userid': 21341588}Surface Temperature9808Dvorište - atmosfera2020-10-01NaT<NA>44.11380015.2372008.0<NA>gps01010000A0E6100000EF38454772792E40742497FF900E464000000000000020405False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Observer AppTrueTrue
6060445187533712018-04-10 00:00:00825166665wet2503011221<NA>2018-04-13 12:55:30.6811882018-04-13 12:55:30.6811882939238-2.60NaN<NA>1079412017-07-10 18:54:08.2167582017-07-10 18:54:08.216747<NA>90.090.0<NA>Etekcity774Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherestudent user - trained<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Large Playground2017-07-10NaT<NA>41.762343-83.585150200.0<NA>other01010000A0E61000004850FC1873E554C0D2AA967494E1444000000000000069401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalseTrue
728291593173701972024-12-06 09:58:008142150439dry13779895021558981212024-12-06 09:59:18.2453872024-12-06 09:59:18.245387703916230.00NaN<NA>8246842024-11-30 19:09:07.3621222024-11-30 19:09:07.362112<NA>NaNNaNshort grass<NA>Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherestudent user - trained<NA>5<NA>24.3501056.7133011.301010000A0E61000004F1E166A4D5B4C4061545227A05938409A99999999992640{'teacher_userid': 113932171}Surface Temperature9808AFIFA2024-11-13NaT<NA>24.35010056.71330011.3<NA>gps01010000A0E61000004F1E166A4D5B4C4061545227A05938409A999999999926405False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Observer AppTrueTrue
731501634863672152025-02-11 10:12:008121494933dry12149495011582209162025-02-11 11:17:44.0891442025-02-11 11:17:44.089144716953132.70NaN<NA>85457302024-12-22 10:23:33.7227902024-12-22 10:23:33.722781<NA>10.010.0sandInfrared Thermometer (LASERGRIP 749)Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenon-student user - trained<NA>5<NA>30.8692929.5870666.201010000A0E61000002ECA6C9049963D4066BD18CA89DE3E40CDCCCCCCCC8C5040NoneSurface Temperature9808Alexandria STEM School2024-10-14NaT<NA>30.86929029.58706066.2<NA>gps01010000A0E61000002ECA6C9049963D4066BD18CA89DE3E40CDCCCCCCCC8C50405False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Observer AppTrueTrue
731611637723672152025-02-18 10:05:008121494933dry12149495011584969292025-02-18 11:31:20.3092412025-02-18 11:31:20.309241718289124.60NaN<NA>85457302024-12-22 10:23:33.7227902024-12-22 10:23:33.722781<NA>10.010.0sandInfrared Thermometer (LASERGRIP 749)Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenon-student user - trained<NA>5<NA>30.8692929.5870666.201010000A0E61000002ECA6C9049963D4066BD18CA89DE3E40CDCCCCCCCC8C5040NoneSurface Temperature9808Alexandria STEM School2024-10-14NaT<NA>30.86929029.58706066.2<NA>gps01010000A0E61000002ECA6C9049963D4066BD18CA89DE3E40CDCCCCCCCC8C50405False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Observer AppTrueTrue
st_idsite_idmeasured_atprotocol_iduseridsurface_conditionorganizationidusertypesubmission_idst_updated_atst_created_atsts_idsample_numbersample_surface_temperature_csample_snow_depth_mmsample_snow_depth_flagversion_idversionsite_version_activated_atversion_datesite_version_commentshomogeneous_site_short_length_mhomogeneous_site_long_length_msurface_cover_typeinstrument_typeprotocol_nameprotocol_modelprotocol_association_nameprotocol_alt_nameprotocol_investigation_areauser_type_descriptionsubmission_commentssubmission_developer_key_idsubmission_access_code_idsubmission_latitudesubmission_longitudesubmission_elevationsubmission_pointsubmission_dataprotocol_set_nameprotocol_set_codesite_namesite_activated_atsite_deactivated_atsite_commentssite_latitudesite_longitudesite_elevationsite_elevation_typesite_location_sourcesite_pointsite_developer_key_idsite_is_citizen_sciencesite_nicknamesite_true_latitudesite_true_longitudesite_true_elevationsite_true_pointsite_photo_measured_atsite_photo_primary_thumb_urlsite_photo_primary_photo_urlsite_photo_photo_datadeveloper_key_namedeveloper_key_is_citizen_sciencePCA_outlier_flag
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